Remote sensing applications in solid waste monitoring and management

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Data may be preliminary. 1 October 2025 V1 Latest version Share on Remote sensing applications in solid waste monitoring and management Authors : Bijeesh Kozhikkodan Veettil [email protected] , Nurzada Amangeldy , Vikram Puri , and Tran Thi Nhu Phuong Authors Info & Affiliations https://doi.org/10.22541/au.175931844.49473178/v1 1066 views 275 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The rapid growth of solid waste generation carries significant environmental and public health challenges globally. Traditional waste monitoring methods are not cost-effective and are limited in spatial and temporal scope. Remote sensing, encompassing satellite, aerial, and unmanned platforms, has emerged as a transformative approach to identify, monitor, and manage solid waste sites. This review provides a comprehensive synthesis of the state-of-the-art in remote sensing applications for solid waste monitoring and management, covering technological developments in the past two decades. We examine sensor platforms (optical, hyperspectral, thermal, SAR, LiDAR), data processing and machine learning workflows, major application domains (landfill mapping, illegal dumping detection, volumetric assessment, thermal anomaly detection, greenhouse gas monitoring, marine plastic tracking), and integration with GIS and IoT systems. The present study also evaluates operational successes, methodological challenges, policy implications, and outlines a research roadmap for enhancing the accuracy, scalability, and real-world adoption of remote sensing in waste management. Remote sensing applications in solid waste monitoring and management Bijeesh Kozhikkodan Veettil 1,2,* , Nurzada Amangeldy 3 , Vikram Puri 4,5 , Tran Thi Nhu Phuong 6 1 Laboratory of Ecology and Environmental Management, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City, Vietnam. Email: [email protected] 2 Faculty of Applied Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City, Vietnam 3 Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana 010008, Kazakhstan. Email: [email protected] 4 School of Computer Science, Duy Tan University, Da Nang, Vietnam. Email: [email protected] 5 Institute of Research and Development, Duy Tan University, Da Nang, Vietnam. 6 Department of Environmental Management, Ho Chi Minh City University of Science (VNU-HCM), Ho Chi Minh City, Vietnam. Email: [email protected] * Corresponding author Abstract : The rapid growth of solid waste generation carries significant environmental and public health challenges globally. Traditional waste monitoring methods are not cost-effective and are limited in spatial and temporal scope. Remote sensing, encompassing satellite, aerial, and unmanned platforms, has emerged as a transformative approach to identify, monitor, and manage solid waste sites. This review provides a comprehensive synthesis of the state-of-the-art in remote sensing applications for solid waste monitoring and management, covering technological developments in the past two decades. We examine sensor platforms (optical, hyperspectral, thermal, SAR, LiDAR), data processing and machine learning workflows, major application domains (landfill mapping, illegal dumping detection, volumetric assessment, thermal anomaly detection, greenhouse gas monitoring, marine plastic tracking), and integration with GIS and IoT systems. The present study also evaluates operational successes, methodological challenges, policy implications, and outlines a research roadmap for enhancing the accuracy, scalability, and real-world adoption of remote sensing in waste management. Keywords : remote sensing; landfill monitoring; waste detection; hyperspectral imaging; machine learning; solid waste management 1. Introduction Generation of solid waste is one of the most persistent environmental issues of the 21 st century, exacerbated by rapid urbanization, industrial expansion, and shifting consumption patterns, which contribute to unprecedented volumes of municipal, industrial, and hazardous waste (Vergara and Tchobanoglous 2012; Marshall and Farahbakhsh 2013; Sharma and Jain 2020; Voukkali et al. 2024). According to global estimates, annual solid waste production is expected to exceed 3.8 billion tons by 2050, placing significant pressure on already overstressed waste management infrastructures (Islam 2025). Poorly managed landfills, open dumping, and illegal waste disposal contribute to severe consequences for soil, water, and air quality, and have direct impacts on human health and biodiversity (Al-Wabel et al. 2022; Siddiqua et al. 2022). Leachate infiltration contaminates water resources, landfill gas emissions (methane, in particular) accelerate global warming, and unmanaged waste often becomes a breeding ground for disease vectors (Siddiqua et al. 2022; Addy et al. 2023; Laoye et al. 2024). Traditional waste monitoring systems depend heavily on manual inspections, field surveys, and sporadic reporting, which are labor-intensive, costly, and have limited coverage (Milu et al. 2025; Yevle and Mann 2025). These methods often fail to provide timely data, hindering the ability of the local authority to respond proactively to emerging waste management challenges (Yong et al. 2023; Yevle and Mann 2025). Furthermore, the lack of continuous monitoring often leads to underreporting of illegal dumping activities, underestimation of landfill capacity usage, and delayed identification of environmental hazards (Ichipi 2023; Cicala et al. 2024). Remote sensing technology offers a paradigm shift in how solid waste is monitored and managed. By using satellites, aerial imagery, and UAV systems, non-invasive, large-scale, and continuous observations that can capture both spatial and temporal dynamics of waste generation, accumulation, and management are possible (Hayat 2023; Cicala et al. 2024; Narzari et al. 2025). The increasing availability of high-resolution imagery, together with advancements in machine learning and cloud-based analytics, allows the precise detection of waste sites, monitoring of volumetric changes, and identification of hazardous conditions such as landfill fires or gas leaks (Divine et al. 2024). From multispectral and hyperspectral imaging capable of discriminating against waste types (Tao et al. 2023; Shiddiq et al. 2023), to synthetic aperture radar (SAR) that can detect landfill boundary changes regardless of weather conditions (Papale et al. 2023), remote sensing tools provide diverse capabilities for waste monitoring and management. UAV-based surveys offer ultra-high-resolution data at flexible temporal intervals, bridging the gap between field data and satellite observations (Zhang and Zhu 2023). Additionally, the integration of remote sensing with IoT devices, such as in-situ gas analyzers and leachate sensors, enables hybrid monitoring systems that combine the strengths of both remote and on-site data sources (Narayanappa et al. 2024; Ali et al. 2025). Globally, several research publications and operational projects have demonstrated the potential of remote sensing techniques in mapping unregulated dumpsites, monitoring waste transport, estimating landfill lifespans, and detecting marine plastic debris (Veettil et al. 2022; Papale et al. 2023; Fraternali et al. 2024). In developing countries, where waste management resources are often limited, the adoption of remote sensing can significantly enhance regulatory oversight and support data-driven decision-making (Bhavani and Gajendra 2024; Raj and Tiwari 2025). The present study synthesizes developments in remote sensing technologies for solid waste monitoring since the early 2010s, highlighting both scientific advancements and real-world applications. We categorized sensor platforms and their suitability for different waste monitoring tasks, explored the role of machine learning in automated waste detection, and evaluated integration with geographic information systems (GIS) for spatial analysis. The review also discusses operational challenges, such as spectral misinterpretation between waste materials and natural features, limitations in ground truth data, and regulatory constraints on UAV usage, and proposes a future roadmap for research and policy integration. By providing a comprehensive understanding of the current state of remote sensing applications in waste management, this paper aims to inform students, researchers, policymakers, and waste management practitioners about the opportunities and limitations of these technologies. Eventually, remote sensing holds the promise of enabling more sustainable, efficient, and transparent waste governance systems worldwide. Figure 1 shows the integrated remote sensing framework Using Satellite, UAV, and IoT for solid waste management. Figure 1 : Integrated Remote Sensing Framework Using Satellite, UAV, and IoT for solid waste management. 2. Remote sensing platforms and sensors for solid waste monitoring Remote sensing for solid waste monitoring is based on a diverse array of platforms and sensors, each offering discrete advantages in terms of spatial coverage, temporal frequency, spectral resolution, and operational flexibility (Veettil et al. 2022; Papale et al. 2023; Fraternali et al. 2024). Understanding these platforms is vital in choosing the right tool for specific waste management applications (Veettil et al. 2022). Satellites, airplanes, and unmanned aerial vehicles (UAVs) are among these platforms (Matese et al. 2015). Figure 2 shows the decision framework for selecting a remote sensing framework. Figure 2 : Decision Framework for Remote Sensing Platform Selection 2.1. Satellite platforms Satellites provide large-scale, high-temporal coverage, making them essential for long-term monitoring (Zhao and Yu 2025). They are broadly categorized into high-resolution commercial satellites (e.g., WorldView series, GeoEye, RapidEye), medium-resolution public missions (e.g., Landsat series, Sentinel-2), and radar satellites (e.g., Sentinel-1, RADARSAT). Optical sensors capture reflected sunlight across multiple wavelengths, enabling the mapping of landfills and the assessment of vegetation health near waste sites (Slonecker et al. 2010; Papale et al. 2023). Hyperspectral satellites (e.g., PRISMA, EnMAP) offer hundreds of narrow bands, enabling material-level discrimination between waste types (Zheng et al. 2018; Yang et al. 2025). SAR satellites penetrate clouds and operate day or night, making them useful in all-weather monitoring of waste sites (Giuseppe 2007). 2.2. Manned aerial platforms Manned aircraft equipped with multispectral, hyperspectral, LiDAR, or thermal cameras provide flexible, high-resolution data acquisition over targeted areas (Panda et al. 2016). Aerial LiDAR can generate precise 3D models of landfill topography, which can support volumetric analysis (Hedley 2009). Thermal cameras detect hotspots, indicating underground fires or active gas emissions (Burke et al. 2019). 2.3. UAV (drone) platforms UAVs have revolutionized localized waste monitoring owing to their affordability, ultra-high resolution (less than 5cm), and on-demand deployment (Fuentes-Peñailillo et al. 2024). Equipped with RGB, multispectral, hyperspectral, or thermal sensors, UAVs enable detailed inspections, rapid post-disaster waste assessments, and detection of illegal dumping areas (Yao et al. 2019). UAVs bridge the spatial gap between satellite imagery and ground-based surveys (Laugier and Casana 2021; Hassan et al. 2023). 2.4. Sensor types Various sensor types that can be used to detect and monitor solid waste and landfill areas are listed below. 1. Multispectral Sensors: Capture multiple broad bands for waste site detection, vegetation monitoring, and land-use changes in landfill areas (Papale et al. 2023). Multispectral sensors are widely used in solid waste remote sensing due to their ability to capture data across several broad spectral bands, typically in the visible, near-infrared, and shortwave infrared regions (Slonecker et al. 2010; Papale et al. 2023). While they provide less spectral data compared to hyperspectral sensors, multispectral instruments (e.g., Landsat, Sentinel-2, and WorldView satellites) offer valuable insights for large-scale waste monitoring at relatively low cost and with high temporal coverage (Papale et al. 2023). These sensors are effective in detecting and mapping landfill extents, differentiating waste-covered surfaces from vegetation or soil, and monitoring changes in land use associated with waste disposal activities (Ottavianelli 2005). Their spectral sensitivity also enables the identification of surface temperature anomalies, vegetation stress caused by leachate seepage, and the spread of uncovered waste (Slonecker et al. 2010; Veettil et al. 2022; Papale et al. 2023). With moderate to high spatial resolution and frequent revisit times, multispectral data can provide a practical and accessible resource for supporting municipal solid waste management, especially when integrated with GIS and machine learning techniques for classification and change detection (Papale et al. 2023; Wang et al. 2024). 2. Hyperspectral Sensors: Offer fine spectral resolution for identifying specific waste with material-level precision, differentiating plastics from organics (Moroni et al. 2025), and playing a critical role in solid waste remote sensing by providing detailed spectral information across hundreds of narrow, contiguous bands, enabling precise identification and characterization of materials (Ottavianelli 2005; Menezes et al. 2024). This high spectral resolution allows for distinguishing between different types of waste, such as plastics, metals, organics, and construction debris, which often appear similar in conventional multispectral imagery (Slonecker et al. 2010; Shiddiq et al. 2023; Menezes et al. 2024). In landfill monitoring, hyperspectral data can be used to detect surface composition, identify hazardous materials, and monitor changes in waste degradation and gas emissions (Ottavianelli 2005; Slonecker et al. 2010; Papale et al. 2023). Airborne and satellite-based hyperspectral sensors are particularly effective in mapping illegal dumping sites and assessing the extent of waste leakage into surrounding environments (Ottavianelli et al. 2005; Papale et al. 2023). When combined with advanced machine learning and spectral unmixing techniques, hyperspectral remote sensing offers robust capabilities for material classification, early detection of pollution sources, and supporting circular economy strategies by identifying recyclable and recoverable waste fractions (Tao et al. 2023). 3. Thermal Infrared Sensors: Detect temperature anomalies, track decomposition heat, and locate landfill fires (Grondona et al. 2022). Thermal remote sensing is increasingly applied in solid waste remote sensing because of its ability to measure surface temperature variations and heat emissions associated with waste degradation and landfill operations (Yan et al. 2014). Solid waste sites often generate significant thermal anomalies due to microbial decomposition, gas emissions, and occasional subsurface fires, which can be effectively detected using thermal infrared (TIR) sensors mounted on satellites, drones, or airborne platforms (Tanda et al. 2020). Thermal information supports identifying hotspots that indicate uncontrolled burning, leachate seepage zones, or methane release points, thus supporting early warning systems for environmental and health risks (Davis et al. 2022). Furthermore, long-term thermal monitoring provides valuable insights into landfill stabilization processes and the effectiveness of waste management practices, such as waste covering and gas recovery (Wang et al. 2012). When combined with optical or SAR data, thermal remote sensing enhances the ability to monitor both the physical and thermal dynamics of solid waste sites, making it a valuable component of integrated waste management strategies (Ottavianelli et al. 2005; Papale et al. 2023; Wang et al. 2024). 4. Synthetic Aperture Radar (SAR): Monitor surface deformation in landfills and detect waste-related infrastructure changes under cloud cover (all weather conditions), smoke, and even partially into waste piles, making it particularly suitable for monitoring landfills and illegal dumping sites in urban and peri-urban environments (Giuseppe 2007). The sensitivity of SAR to surface roughness, moisture content, and structural changes allows for the detection of waste accumulation, compaction, and leachate seepage over time (Ottavianelli 2007). Advanced processing techniques, such as interferometric SAR (InSAR) and polarimetric SAR (PolSAR), further enhance its application by enabling subsidence monitoring of landfill sites, structural deformation analysis, and material classification (Ottavianelli 2007; Papale et al. 2023). Consequently, SAR provides a reliable and continuous monitoring approach that complements optical remote sensing in integrated solid waste management systems (Ottavianelli et al. 2005). 5. LiDAR: Create detailed elevation models, enabling volumetric change detection in landfill areas (Pasternak et al. 2023). LiDAR sensors are highly effective in solid waste remote sensing due to their ability to generate precise three-dimensional representations of terrain and surface features (De Wet 2016). By emitting laser pulses and measuring their return times, LiDAR provides high-resolution elevation data that can be used to map landfill boundaries, monitor waste pile volumes, and detect topographic changes caused by waste accumulation or subsidence (Pasternak et al. 2023). Airborne and drone-based LiDAR systems are particularly useful for volumetric analysis, enabling accurate estimation of landfill capacity and remaining space, which supports planning and operational efficiency (Alsayed 2024). Additionally, LiDAR can penetrate vegetation covers to reveal hidden waste deposits or illegal dumping sites, making it valuable for environmental enforcement (Slonecker et al. 2010; De Wet 2016; Vambol et al. 2019). When integrated with multispectral or hyperspectral data, LiDAR enhances classification accuracy by combining structural and spectral information, offering a comprehensive approach to monitoring and managing solid waste sites (Ottavianelli et al. 2005; Menezes et al. 2024). 2.5. Platform-sensor matching Selecting a platform-sensor combination depends on the monitoring goal, spatial scale, and resource constraints (Gómez and Green 2017). For example, detecting marine plastic debris may rely on hyperspectral satellites, while tracking illegal dumpsites in urban areas might require UAV-based RGB and thermal sensors (Tanda et al. 2020; Veettil et al. 2022). These platforms and sensors, when integrated with advanced analytics, form the technological backbone for modern solid waste monitoring systems (Bhardwaj and Gupta 2024). Platform-sensor matching is a critical consideration in remote sensing, as the effectiveness of data acquisition depends on aligning sensor capabilities with the operational needs of waste monitoring (Wen et al. 2003). For example, satellite platforms equipped with multispectral sensors (e.g., Landsat, Sentinel-2) are well-suited for regional-scale monitoring of landfill expansion and land use changes, while very high-resolution commercial satellites (e.g., WorldView2) provide detailed mapping of urban dumpsites. Airborne platforms, including drones and aircraft, are ideal for deploying hyperspectral, LiDAR, or thermal sensors (Ndehedehe 2022) to capture fine-scale information on waste composition, volumetric estimates, or thermal hotspots. Ground-based mobile LiDAR and hyperspectral systems can be used for site-specific surveys, offering detailed validation and supporting localized management practices (Farhan et al. 2024). Matching the right platform with the appropriate sensor ensures optimal spatial, spectral, and temporal resolution for detecting, classifying, and monitoring solid waste, thereby enhancing both cost-effectiveness and the accuracy of decision-making in waste management (Raj and Tiwari 2025). 3. Application domains Remote sensing technology has been applied to a wide range of solid waste monitoring and management domains, offering solutions across the waste lifecycle from production to disposal (Dutta and Goel 2017; Singh 2019). Each application leverages explicit platform and sensor combinations tailored to the exclusive detection and analysis requirements. Figure 3 highlights the sequence architecture workflow for integrated remote sensing and AI/ML-based environmental monitoring. Figure 3 : System architecture workflow for integrated remote sensing and AI/ML-based environmental monitoring. 3.1. Landfill mapping and monitoring Satellites, aerial surveys, and UAVs enable precise mapping of landfill boundaries, expansion rates, and operational zones (Filkin et al. 2021; Wang et al. 2024). High-resolution optical imagery identifies surface features, while LiDAR provides 3D topography for volumetric assessments (Tarolli 2014; Gharineiat et al. 2022). Time-series analysis supports the tracking of landfill growth and compliance with closure requirements. 3.2. Illegal dumping detection Unauthorized waste disposal can be identified using UAV-based RGB and thermal imagery, as well as high-resolution satellite data (Filkin et al. 2021; Wang et al. 2024). Change detection algorithms highlight new anomalies in open spaces, riverbanks, and industrial areas. Thermal sensors help detect freshly dumped waste due to temperature contrasts (Grondona et al. 2022). 3.3. Volumetric assessment of waste piles LiDAR and photogrammetry from UAV or aerial platforms generate digital elevation models (DEMs) that allow for the precise calculation of waste volumes (Filkin et al. 2021, 2022; Gharineiat et al. 2022). This aids in operational planning, landfill capacity estimation, and compliance reporting. 3.4. Thermal anomaly detection Thermal infrared sensors detect hotspots in waste facilities, which can signal underground fires, decomposition heat, or gas emissions (Grondona et al. 2022). Continuous monitoring for thermal anomalies helps prevent hazardous incidents (Grondona et al. 2022). 3.5. Greenhouse gas monitoring SAR and hyperspectral sensors, combined with ground-based measurements, help quantify CH 4 and CO₂ emissions from waste sites (Ottavianelli 2005; Sugavaneshwaran et al. 2024). Thermal and hyperspectral data can detect gas plumes, supporting climate change mitigation strategies (Scafutto et al. 2018). 3.6. Marine and riverine plastic tracking Hyperspectral and multispectral satellite imagery detect floating plastic debris in oceans and rivers (Veettil et al. 2022). UAV surveys provide local-scale mapping, aiding cleanup operations and policy enforcement (Veettil et al. 2022). 3.7. Disaster waste assessment Following natural disasters, UAV and aerial platforms rapidly assess waste accumulation from collapsed infrastructure (Ezequiel et al. 2014; Zhang et al. 2021). This supports emergency response planning and prioritization (Zhang et al. 2021). 3.8. Waste-to-energy facility monitoring Thermal and optical remote sensing can monitor operational efficiency and detect anomalies in waste-to-energy plants, ensuring compliance and safety (Muri and Hjelme 2022; Mei et al. 2023). By spanning these application domains, remote sensing offers a multi-scale, multi-sensor toolkit for modern solid waste management, enhancing decision-making, efficiency, and sustainability (Dritsas and Trigka 2025). 4. Data processing and analytics The effective use of remote sensing for solid waste monitoring and management hinges not only on the choice of platforms and sensors but also on the data processing and analytical workflows that convert raw imagery into ground reality information (Dritsas and Trigka 2025; Raj and Tiwari 2025). The processing steps encompass data acquisition, preprocessing, feature extraction, classification, change detection, and integration with other geospatial datasets. Recent advances in computational capacity, cloud-based processing, machine learning, and AI have significantly expanded analytical capabilities in remote sensing (Dritsas and Trigka 2025). The synergy of advanced preprocessing, multi-sensor fusion, AI-driven analytics, and integration with GIS and IoT systems has transformed remote sensing into a powerful operational tool for solid waste monitoring and management (Dritsas and Trigka 2025). These developments are not only enhancing accuracy and efficiency but also enabling near-real-time decision-making, which is a critical factor in addressing today’s waste management challenges (Ncube and Ngulube 2024). 4.1. Data preprocessing Raw remote sensing data often contains distortions due to sensor noise, atmospheric effects, geometric misalignments, and radiometric inconsistencies (Congalton 1991). Preprocessing includes radiometric correction, atmospheric correction algorithms (e.g., FLAASH, QUAC), geometric correction to align imagery with a geographic coordinate system, and orthorectification for adjusting topographic relief (Sharma et al. 2025). For UAV imagery, structure-from-motion (SfM) techniques are applied to generate orthomosaics (Turner et al. 2012). 4.2. Feature extraction Feature extraction transforms preprocessed imagery into meaningful indicators of waste presence and characteristics (Arebey et al. 2012). For optical and hyperspectral data, spectral indices such as NDVI, NDWI, and plastic debris indices are computed (Guo and Li 2020; Veettil et al. 2022). Texture metrics derived from gray-level co-occurrence matrices (GLCM) and shape descriptors help to differentiate waste piles from natural features (Thiruchittampalam et al. 2024). 4.3. Image classification Image classification algorithms assign each pixel or object to a waste-related or non-waste category (Torres and Fraternali 2021). Traditional classifiers such as maximum likelihood (ML) and support vector machines (SVMs) are still being used, whereas deep learning methods, particularly convolutional neural networks (CNNs), have revolutionized classification accuracy (Hasan et al. 2019). Object-based image analysis (OBIA) integrates spectral, spatial, and contextual information, making it specifically valuable for high-resolution UAV imagery (Ventura et al. 2018). 4.4. Change detection Time-series imagery allows detection of changes in waste site extent, volume, or composition (Yan et al. 2014). Change detection techniques include image differencing, post-classification comparison, and change vector analysis (Lu et al. 2004). SAR data support all-weather change detection, particularly useful in watching illegal dumping and landfill expansion (Ottavianelli 2007). 4.5. Volumetric and 3D analysis Volumetric and 3D analysis in remote sensing of solid waste plays a crucial role in quantifying the size, capacity, and spatial dynamics of waste accumulation. LiDAR point clouds and UAV-derived DEMs enable volumetric analysis of waste piles and landfill capacity (Tsachouridis et al. 2025). These models also facilitate slope stability assessment and infrastructure planning (Tsachouridis et al. 2025). Unlike traditional 2D mapping, volumetric assessments provide a more realistic understanding of the physical extent of waste, helping urban planners, engineers, and policymakers optimize landfill design, predict lifespan, and evaluate potential hazards such as slope instability or leachate overflow (Chen 2024). This makes 3D remote sensing a powerful tool for improving the sustainability and safety of solid waste management systems. 4.6. Data fusion Integrating multiple data sources improves monitoring accuracy (De Sy et al. 2012; Chen et al. 2017). For instance, fusing optical imagery with SAR enhances change detection in cloudy conditions (Gao et al. 2021). In addition, combining hyperspectral data with thermal imagery supports simultaneous identification of material types and hotspots (Segl et al. 2003). 4.7. Machine learning and artificial intelligence (AI) Supervised, unsupervised, and semi-supervised learning methods are applied for automated waste detection (Liu et al. 2020). Deep learning models trained on large, annotated datasets can detect subtle features indicative of waste, while transfer learning allows adaptation to new geographic areas with limited training data (Torres and Fraternali 2021; Liu et al. 2024). A detailed application of machine learning methods in solid waste management is provided in the next section. 4.8. Cloud and edge computing Platforms like Google Earth Engine (GEE) facilitate large-scale processing of multi-temporal datasets without local hardware limitations (Zhao et al. 2021; Kumar et al. 2025). Edge computing on UAVs enables real-time waste detection during flight, useful for rapid-response scenarios such as a potential waste-related hazard (Amesho et al. 2024). 4.9. Integration with GIS and IoT Processed remote sensing outputs are integrated into GIS platforms for spatial analysis, visualization, and decision-making (Chatrabhuj et al. 2024). Linking with IoT sensors (e.g., methane detectors) provides a more comprehensive waste monitoring framework (Dwivedi et al. 2025). 4.10. Validation and accuracy assessment Validation and accuracy assessment are key components in the remote sensing of solid waste, ensuring that derived information is reliable and suitable for decision-making. Validation involves comparing remotely sensed outputs (e.g., detected waste sites, classification maps, estimated volume) with independent reference data collected through ground surveys, UAV imagery, or high-resolution satellite observations. Accuracy is evaluated using confusion matrices, kappa statistics, and field validation (Foody 2020). Ground truth data is critical for ensuring that remote sensing-derived waste metrics are reliable for policy and operational use (Chen et al. 2021). To enhance accuracy, multi-source data integration, higher spatial and spectral resolution imagery, and machine learning classifiers are often used in remote sensing (Chen et al. 2017). 5. Machine learning methods in Solid Waste Management As the volume of municipal solid waste (MSW) continues to grow rapidly worldwide, the need for effective systems to monitor, classify, and manage it has increased accordingly. In this context, machine learning (ML) methods-capable of automating data analysis, building predictive models, and optimizing technological processes-have become essential tools. Unlike traditional statistical approaches (Reichstein et al. 2019), ML algorithms can work with high-dimensional and heterogeneous data ranging from satellite imagery to laboratory indicators of leachate and parameters of processing facilities. 5.1. ML algorithms Among core ML algorithms, the Support Vector Machine (SVM) (Meza et al. 2019; Sun et al. 2023) and Random Forest (RF) (Rutqvist et al. 2020) are used to classify and map landfill sites. Leveraging multispectral and radar data, these algorithms have proven effective in identifying illegal dumping areas and assessing the spatial characteristics of MSW. Artificial Neural Networks (ANNs) have been used to predict the efficiency of extracting humic substances from MSW leachate (Rezaeinia et al. 2023), as well as to model interactions between waste and engineering materials such as geogrids (Sarkar et al. 2023). These studies highlight ML’s potential to uncover complex nonlinear relationships that classical regression approaches (Reichstein et al. 2019) cannot adequately capture. 5.2. Deep Learning Deep Learning (DL), which has been developing rapidly, is now widely applied to MSW management (Dawar et al. 2025). Unlike traditional ML, DL architecture can process large volumes of visual and sensor data, which is crucial for separating waste streams, analyzing dumpsites, and optimizing recycling processes. DL algorithms enable automatic identification of various waste categories and the deployment of “smart” sorting systems, with YOLO-based models seeing particularly wide adoption. For example, in a study from Ethiopia, models based on YOLOv4 and YOLOv4-tiny were trained on a dataset of 3,529 images divided into seven classes (cardboard, glass, metal, organic, paper, plastic, and general trash) and were tested on images, videos, and webcam streams. The results confirmed YOLOv4’s strong potential for accurate real-time waste detection within intelligent waste management systems (Meron et al. 2023). During the COVID-19 period, a mobile robot design based on YOLO architecture (YOLOv3, YOLOv3-tiny-prn, YOLOv4, and YOLOv4-tiny-416) was also proposed to ensure safer handling of medical waste (Zhang et al. 2025). The robot, equipped with a six-DoF manipulator, a grasping algorithm, and a lidar sensor, can automatically detect, collect, and safely transport waste items. Tests in ROS and the Gazebo simulator demonstrated the approach’s potential to automate waste collection and create safer environments in medical facilities. A recently proposed anchor-free YOLO architecture targets efficient detection of small, densely distributed objects in complex backgrounds. The model incorporates a streamlined backbone, multi-scale detection, an improved CJAM (Coordinate Joint Attention Mechanism), and additional feature-enhancement modules (DPFE, IRFE, PRes2Net). An auxiliary network fuses lower- and mid-level features into high-level semantics, while a Swin Transformer in the neck captures global context (Xu and Vu 2023). In solid waste tasks, ResNet-50 is often used as a feature extractor, and the MSWNet architecture built on ResNet-50 has demonstrated high-accuracy sorting of real waste images via transfer learning (Lin et al. 2023). A model combining ResNet-50 with Feature Pyramid Network (FPN) components has also been used to classify aerial scenes into “landfill / non-landfill,” achieving an average precision of 94.5% and an F1 score of 88.2% on an expert-labeled dataset (Torres and Fraternali 2021). Class Activation Maps (CAMs) further showed that the network focuses on the same image regions as human analysts, helping accelerate photo-interpretation in environmental monitoring. Beyond YOLO and ResNet, Convolutional Neural Networks (CNNs) are widely used to classify waste from imagery with high accuracy (Altikat et al. 2021). CNN-based models automatically extract spatial features from images, enabling reliable classification across visual datasets (Zhang et al. 2021; Tiwari and Dubey 2022) [14, 15]. In addition, hybrid CNN–LSTM models enable robust forecasting of waste volumes and support improved management systems (Lin et al. 2021; Li and Chen 2023). 5.3 Hybrid solutions Remote sensing (RS) data constitute multi-form information gathered from different sensors (optical, SAR, thermal, hyperspectral) at various resolutions and revisit cycles. This heterogeneity poses significant challenges for classical methods-due to illumination variability, seasonality, cloud cover, small and densely packed objects, and weak/noisy signatures. In this respect, Deep Learning (DL)-capable of automatically learning multi-scale, spatio-spectral, and temporal patterns-stands out as the most effective approach for MSW monitoring. At the same time, practical systems rarely rely on a single paradigm and typically require hybrid solutions-DL + multisensor fusion (Batchu et al. 2023), DL + optimization (Zhang et al. 2022), and DL + physics/process constraints (Cuomo et al. 2022). Such composite approaches mitigate data-quality variability, strengthen generalization, and suit real-time operation (UAVs, edge devices, video streams). 6. Challenges in using remote sensing for solid waste management The application of remote sensing to solid waste monitoring and management faces several technical, operational, economic, and institutional challenges that influence its accuracy, scalability, and adoption. While remote sensing offers substantial potential for waste monitoring, overcoming these challenges requires a combination of technological innovation, capacity building, policy reform, and sustainable financing models. 6.1. Technical limitations Spatial resolution constraints limit the detection of small-scale illegal dumps, especially from medium-resolution satellites such as the Landsat series (Glanville and Chang 2015). High-resolution data can be used to solve this issue, but often at higher costs and limited revisit times (Glanville and Chang 2015). Spectral confusion between waste materials and natural or non-waste features also complicates the classification process (Pati et al. 2025). Furthermore, hyperspectral sensors generate large datasets requiring advanced processing capabilities (Adão et al. 2017). 6.2. Atmospheric and environmental interferences Cloud cover, haze, and varying illumination conditions affect optical imagery and time-series analysis, while seasonal vegetation growth can obscure waste sites (Slonecker et al. 2010; Mancino et al. 2022). Thermal sensors are influenced by variations in surface emissivity (Jin and Liang 2006), and SAR data may be affected by speckle noise (Choi and Jeong 2019). 6.3. Data availability and access While some satellite data are freely available, high-resolution commercial imagery remains costly, creating barriers for municipalities in low- and middle-income countries (Haenssgen 2015). UAV surveys may be restricted by regulatory frameworks, especially in sensitive areas, such as coastal zones (Stöcker et al. 2017). 6.4. Processing and analytical complexity Advanced analytical methods, particularly AI-driven approaches, require skilled personnel and computational resources (Selvarajan 2021). In many regions, limited technical capacity hinders operational use. 6.5. Ground truthing and validation Accurate field data collection for validation can be logistically difficult and costly, especially in hazardous or inaccessible waste sites (Silvestri and Omri 2008). Without robust validation, remote sensing outputs may lack credibility for decision-making (Morales-Barquero et al. 2019). 6.6. Integration with existing systems Incorporating remote sensing data into waste management workflows requires compatible GIS infrastructure, interoperable formats, and staff training (Raj and Tiwari 2025). These infrastructures may not be readily available in many developing countries. 6.7. Policy and institutional barriers Lack of supportive policy frameworks, limited inter-agency coordination, and insufficient funding impede the adoption of remote sensing in solid waste management (Ruuhulhaq 2024). Moreover, some jurisdictions may lack clear legal guidelines for UAV operations and data sharing (Stöcker et al. 2017). 6.8. Temporal gaps in monitoring Infrequent data acquisition can miss short-term illegal dumping activities (Selani 2017). Persistent monitoring demands high revisit rates, which can be costly. 6.9. Ethical and privacy concerns High-resolution imagery, especially from UAVs, raises privacy concerns that must be addressed through responsible data governance (Sindiramutty et al. 2024). Competition among corporates may influence such concerns. 6.10. Sustainability of monitoring programs Long-term remote sensing programs require sustained funding, technical maintenance, and periodic technology upgrades (Gail 2007). This is a key challenge that many waste authorities will struggle to meet. 7. Future directions It is widely believed that the next decade will see remote sensing playing a more integrated, intelligent, and predictive role in solid waste monitoring and management (Singh 2019; Divine et al. 2024). Key research and development trends are anticipated to shape the field. The future of remote sensing in solid waste management will be characterized by the convergence of sensors, analytics, policy, and public engagement, leading to more efficient, transparent, and sustainable waste management systems. 7.1. Multi-source data fusion Combining optical, thermal, SAR, hyperspectral, and LiDAR datasets will enhance material differentiation, volumetric estimates, and temporal coverage (Li and Guo 2016). Data fusion techniques, including deep learning-based integration, will reduce uncertainty and improve feature detection (Vemuri et al. 2021; Hussain et al. 2024). 7.2. AI-driven analytics Machine learning and deep learning models can be advanced to achieve fully automated waste site detection, change monitoring, and predictive modeling (Rutqvist et al. 2020). Transfer learning, active learning, and explainable AI will improve model adaptability to different geographic and socio-economic contexts (Hall et al. 2022). 7.3. Real-time UAV and IoT integration UAVs equipped with real-time processing capabilities and linked to IoT-enabled waste bins, sensors, and environmental monitors will create continuous monitoring networks (Rao et al. 2025). This integration will enable dynamic responses to instances of illegal dumping or landfill anomalies. 7.4. Hyperspectral miniaturization Advances in sensor miniaturization will enable affordable hyperspectral imaging on UAVs, facilitating material-level classification of waste types at a high spatial resolution for municipal-scale operations (Kiyokawa et al. 2022). 7.5. Cloud-based processing and open data Cloud platforms, such as Google Earth Engine, will continue to democratize access to remote sensing data collection and analysis, enabling municipalities without high-end hardware to conduct sophisticated investigations (Adhikari 2025). 7.6. Policy-driven innovation Governments will increasingly incorporate remote sensing into waste management regulations, using it for compliance monitoring, landfill permitting, and environmental impact assessments (Singh 2019). 7.7. Citizen science and crowdsourcing Public participation through smartphone apps and crowdsourced image annotation will enhance training datasets and monitoring waste coverage, especially in low-resource settings (Fotovvatikhah et al. 2025). 7.8. Environmental and social safeguards Ethical frameworks and privacy-protecting algorithms must be developed to address concerns over surveillance while enabling high-resolution monitoring (Fabrègue and Bogoni 2023). 7.9. Climate change and circular economy linkages Remote sensing will play a role in tracking waste-related greenhouse gas emissions, supporting circular economy planning, and linking waste monitoring with broader sustainability agendas (Raj and Tiwari 2025). 7.10. Persistent monitoring via nanosatellites Persistent monitoring via nanosatellites offers a transformative approach to remote sensing of solid waste by enabling high-frequency, cost-effective, and scalable observations of waste dynamics. Constellations of nanosatellites will be useful for daily to sub-daily monitoring at moderate resolutions, complementing high-resolution targeted observations from UAVs (Katkani et al. 2022). Unlike traditional large satellites with longer revisit times and higher operational costs, nanosatellites can provide daily or even sub-daily coverage (Aati and Avouac 2020) of urban and rural areas, which is useful for near-real-time tracking of illegal dumping, landfill expansion, and waste transport activities. 8. Policy and governance related to remote sensing in solid waste management Effective policy and governance frameworks are essential to scaling up and sustaining the use of remote sensing in solid waste monitoring and management (De Leeuw et al. 2010; Raj and Tiwari 2025). While technology provides valuable capabilities, its integration into decision-making processes depends on legal, institutional, and socio-political factors (Trudić et al. 2025). In short, effective policy and governance will be the bridge between technical feasibility and real-world impact, ensuring that remote sensing develops into an embedded, transparent, and accountable component of modern solid waste management systems. 8.1. Regulatory frameworks for remote sensing in waste management Many countries are yet to establish explicit legal mandates for the use of remote sensing in waste governance (De Leeuw et al. 2010; Glanville and Chang 2015). Regulatory provisions could formalize the use of satellite and UAV imagery in landfill permitting, illegal dumping enforcement, and compliance monitoring (Cicala et al. 2024; Filkin et al. 2021). Such frameworks should address data access, ownership, and admissibility as legal evidence. 8.2. Institutional roles and coordination Waste management often involves multiple agencies, including environmental regulators, municipal authorities, public health departments, and law enforcement (Nwachukwu et al. 2013). Clear allocation of responsibilities for remote sensing data collection, analysis, and enforcement can prevent duplication and ensure coherent responses (De Leeuw et al. 2010; Legai 2024). 8.3. Funding mechanisms Sustainable financing is needed to maintain continuous monitoring programs (Ferri and Acosta 2019). Options include public–private partnerships, polluter-pays schemes, and integration of remote sensing costs into waste service contracts (Mmereki 2018). 8.4. International and regional cooperation Cross-border waste trafficking and marine debris highlight the need for multinational cooperation (Dill and Kopsick 2014; Albayrak et al. 2021; Wang et al. 2021). Regional satellite data-sharing agreements, such as those in the EU’s Copernicus program, could be adapted for collaborative waste monitoring initiatives. 8.5. Policy-driven technological innovation Governments can drive adoption through innovation incentives, such as grants for AI-based waste detection algorithms or subsidies for municipal UAV fleets (Lakhouit 2025). Policy can also mandate open data sharing to accelerate research. 8.6. Data governance, privacy, and ethics The use of high-resolution imagery raises privacy concerns, especially in urban areas (Denis and Pietro 2025). Governance frameworks must balance environmental surveillance with protections for individual rights. Ethical guidelines should also ensure that remote sensing is not misused for discriminatory enforcement. 8.7. Linking remote sensing to circular economy policies Waste monitoring via remote sensing can directly inform circular economy targets, track recycling rates, detect leakages into the environment, and identify hotspots for material recovery infrastructure (Menezes et al. 2024). 8.8. Capacity building and training National and local governments need skilled personnel to interpret and act on remote sensing outputs (De Leeuw et al. 2010). This requires consistent investment in technical training, university curricula, and public sector–academic partnerships. 8.9. Legal precedents and case studies Several jurisdictions have begun using UAV imagery in environmental court cases to prove illegal dumping (Lega et al. 2014; Mager and Blass 2022). Documenting and disseminating these precedents will help standardize evidentiary use. 8.10. Adaptive governance for emerging technologies As nanosatellites, AI-driven analytics, and real-time UAV systems become mainstream, governance essentially must remain flexible, enabling the adoption of new capabilities without lengthy bureaucratic delays. 9. Conclusions The integration of remote sensing into solid waste monitoring and management characterizes a paradigm shift from reactive to proactive environmental stewardship. By leveraging multi-scale platforms, ranging from satellites to UAVs, combined with advanced analytical tools, authorities can achieve continuous, cost-effective, and spatially comprehensive waste monitoring. Remote sensing technology enables the detection of illegal dumping, the assessment of landfill volumes, the monitoring of thermal anomalies that indicate fires or decomposition hotspots, and the tracking of waste leakage into terrestrial and marine environments. Despite these benefits, challenges persist, including data accessibility, sensor limitations under certain environmental conditions, technical skill requirements, and the integration of data into legal and governance frameworks. Addressing these issues will require targeted policy interventions, enhanced inter-agency coordination, and capacity building at both local and national levels. The future path of remote sensing in waste management is auspicious, with emerging technologies such as hyperspectral nanosatellites, real-time UAV analytics, and AI-driven object detection designed to expand the scope and precision of monitoring capabilities. Integrated with the growing emphasis on circular economy policies, remote sensing can play a vital role in optimizing waste management systems, reducing environmental impacts, and achieving sustainability targets. Modern machine learning methods turn remote sensing data streams into decision-ready intelligence. Deep learning models deliver fast, scalable detection and segmentation of dumpsites, pixel-level change mapping, volumetric estimation from UAV-derived digital elevation models, and early warning via anomaly detection. Hybrid pipelines shift monitoring from periodic surveys to operations that are close to real-time. In a nutshell, remote sensing is not merely a technological tool but also an enabler of informed decision-making, environmental accountability, and robust waste governance. To fully realize its potential, sustained investment, inclusive policy design, ethical safeguards, and collaborative innovation across science, industry, and government will be essential. Declarations and statements Authors’ contributions All authors contributed equally to the manuscript. Funding No funding to declare. Data availability Data will be available on request. Ethics approval/declarations Not Applicable. Consent to participate Not Applicable. Consent for publication Not Applicable. 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Keywords hyperspectral imaging landfill monitoring machine learning solid waste management waste detection Authors Affiliations Bijeesh Kozhikkodan Veettil [email protected] Van Lang University View all articles by this author Nurzada Amangeldy L N Gumilyov Eurasian National University Faculty of Physics and Technology View all articles by this author Vikram Puri Duy Tan University View all articles by this author Tran Thi Nhu Phuong University of Science View all articles by this author Metrics & Citations Metrics Article Usage 1066 views 275 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Bijeesh Kozhikkodan Veettil, Nurzada Amangeldy, Vikram Puri, et al. Remote sensing applications in solid waste monitoring and management. Authorea . 01 October 2025. DOI: https://doi.org/10.22541/au.175931844.49473178/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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