Research progress in remote sensing monitoring and assessment of solid waste

HAN Zhi-long, WANG Yi-fei, LI Ping, LI Lian-wei, YIN Shou-jing

China Environmental Science ›› 2026, Vol. 46 ›› Issue (3) : 1434-1443.

PDF(529 KB)
PDF(529 KB)
China Environmental Science ›› 2026, Vol. 46 ›› Issue (3) : 1434-1443.
Solid Waste

Research progress in remote sensing monitoring and assessment of solid waste

  • HAN Zhi-long1, WANG Yi-fei2, LI Ping1, LI Lian-wei1, YIN Shou-jing1,2
Author information +
History +

Abstract

Primarily based on relevant literature from the Web of Science core database, it analyses the volume, content, and evolving trends of global and national research publications within this field. From an application perspective, solid waste remote sensing research is categorised into three domains: identification of solid waste disposal sites, site selection and suitability assessment, and environmental impact monitoring of disposal sites. Progress and emerging trends within each domain are systematically examined. Analysis reveals that current landfill identification approaches using low-resolution imagery predominantly employ GIS analysis and index construction, while high-resolution imagery applications frequently utilise deep learning techniques (object recognition, semantic segmentation). For site selection and suitability assessment, GIS platforms are commonly employed to conduct multi- criteria decision analysis integrating remote sensing indices (NDVI, NDWI, etc.) and topographical features (roads, water bodies, residential areas, etc.). Environmental monitoring primarily focuses on vegetation stress, surface thermal anomalies, and methane emissions. Current solid waste remote sensing research faces limitations in operationalising identification applications, lacks universally applicable site selection criteria, and has restricted environmental impact monitoring parameters. Building upon this, recommendations for advancing solid waste remote sensing research are proposed, aligned with environmental management requirements.

Key words

solid waste / remote sensing / identification / site selection / environmental impact / review / landfill site

Cite this article

Download Citations
HAN Zhi-long, WANG Yi-fei, LI Ping, LI Lian-wei, YIN Shou-jing. Research progress in remote sensing monitoring and assessment of solid waste[J]. China Environmental Science. 2026, 46(3): 1434-1443

References

[1] 生态环境部.关于发布《固体废物分类与代码目录》的公告:公告2024年第4号[EB/Z].(2024-01-22)[2025-08-08].https://www.mee.gov.cn/xxgk2018/xxgk/xxgk01/202402/t20240201_1065530.html. Ministry of Ecology and Environment. Notice on the Publication of the ‘Solid Waste Classification and Code Catalogue’: Notice No. 4of 2024[EB/Z].(2024-01-22)[2025-08-08].https://www.mee.gov.cn/xxgk2018/xxgk/xxgk01/202402/t20240201_1065530.html.
[2] 郭伊均.加快推动典型大宗固体废弃物综合利用处置进一步促进“无废城市”高标准建设 [J]. 环境保护, 2024,52(14):9-11. Guo Y J.Accelerate the promotion of comprehensive utilization and disposal of typical bulk solid waste to further promote the high- standard construction of”waste-free cities”. [J]. Environmental protection, 2024,52(14): 9-11.
[3] 毛盼,余嘉琦,田义宗.卫星遥感在固体废物监管中的应用 [J]. 再生资源与循环经济, 2024,17(8):21-23. Mao P, Yu J Q, Tian Y Z. The application of satellite remote sensing in solid waste management [J]. Recyclable Resources and Circular Economy, 2024,17(8):21-23.
[4] Lyon J G. Use of maps, aerial photographs, and other remote sensor date for practical evaluations of hazardous waste sites [J]. Photogrammetric Engineering & Remote Sensing, 1987,53(5):515.
[5] Garofalo D, Wobber. Solid-waste and remote-sensing [J]. Photogrammetric Engineering and Remote Sensing, 1974,40(1):45-59.
[6] Karimi N, Ng K T W, Richter A, et al. Development and application of an analytical framework for mapping probable illegal dumping sites using nighttime light imagery and various remote sensing indices [J]. Waste Management (New York, N.Y.), 2022,143:195-205.
[7] Karimi N, Ng K T W. Mapping and prioritizing potential illegal dump sites using geographic information system network analysis and multiple remote sensing indices [J]. Earth, 2022,3(65):1123-1137.
[8] De La Rosa-Belmonte S J, Palafox-Juárez E B, Torrescano-Valle N, et al. Spatial analysis to identify unauthorized municipal solid waste disposal sites in rural areas of southern Mexico [J]. Waste Management & Research: The Journal of the International Solid Wastes and Public Cleansing Association, Iswa, 2024: 734242X241285421.
[9] Gao S J, Chen Y H, Li K N, et al. Mapping opencast iron mine and mine solid waste based on a new spectral index from medium spatial resolution satellite data [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021,14:1.
[10] Li Z L, Guo H D, Zhang L, et al. Time-series monitoring of dust-proof nets covering urban construction waste by multispectral images in Zhengzhou, China [J]. Remote Sensing, 2022,14(15):3805.
[11] Kazaryan M, Semenishchev E, Voronin V. The underground surface analysis of waste disposal objects based on the neural network image processing methods[C]//Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVIII. SPIE, 2022, 12094: 365-375.
[12] Zhou S Y, Mou L C, Hua Y S, et al. Can we use deep learning models to identify the functionality of plastics from space?[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 123:103491.
[13] Sharma K, Sood M. Monitoring, classification and analysis of waste disposal sites using machine learning [J]. Procedia Computer Science, 2024,235:1558-1567.
[14] Liu Y, Ren Y, Wei C, et al. Study on monitoring of informal open-air solid waste dumps based on Beijing-1images [J]. J. Remote Sens, 2009,13:320-326.
[15] Silvestri S, Omri. A method for the remote sensing identification of uncontrolled landfills: formulation and validation [J]. International Journal of Remote Sensing, 2008,29(4):975-989.
[16] Cadau E G, Putignano, et al. Simdeo: An integrated system for landfill detection and monitoring using EO data [C]//IEEE International Geoscience and Remote Sensing Symposium, 2013.
[17] Yonezawa C. Possibility of monitoring of waste disposal site using satellite imagery [J]. Journal of Integrated Field Science, 2009,6: 23-28.
[18] Zhou X, Wang D, Krähenbühl P. Objects as points[J]. arXiv preprint arXiv:2019,1904.07850.
[19] Dai J, Qi H, Xiong Y, et al. Deformable convolutional networks [C]//Proceedings of the IEEE international conference on computer vision. 2017:764-773.
[20] Ren S Q, et al. Faster R-CNN: Towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017,39(6):1137-1149.
[21] Torres R N, Fraternali, et al. Learning to identify illegal landfills through scene classification in aerial images [J]. Remote Sensing, 2021,13(22):4520.
[22] Torres R N, Fraternali P. AerialWaste dataset for landfill discovery in aerial and satellite images [J]. Scientific Data, 2023,10(1):1-14.
[23] Niu B, Feng Q, Yang J, et al. Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach [J]. Geocarto International, 2023,38(1):2164361.
[24] Zhuo Z, Ma S J, Chen J J, et al. An easy and fast method for landfill identification by image-based deep learning [J]. Resources, Conservation and Recycling, 2025,36(7):3131-3145.
[25] Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation [J]. 2015.
[26] Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation [J]. 2018.
[27] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection [J]. 2017.
[28] Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017,39(12):2481-2495.
[29] Yang K, Zhang, et al. Automatic identification method of construction and demolition waste based on deep learning and Gaofen-2data [C]//2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission III, 2022.
[30] Lv S W, Liu, et al. Remote sensing image recognition of dust cover net construction waste: A method combining convolutional block attention module and U-Net [J]. Sensors and Materials, 2024,36(7): 3131-3145.
[31] Zhang C Q, Zhou L, Du M Y, et al. A cross-channel multi-scale gated fusion network for recognizing construction and demolition waste from high-resolution remote sensing images [J]. International Journal of Remote Sensing, 2022,43(12):4541-4568.
[32] Zeng D, Zhang S, Chen F S, et al. Multi-scale CNN based garbage detection of airborne hyperspectral data [J]. IEEE Access, 2019, 7:104514-104527.
[33] Zhou L M, Rao X H, Li Y H, et al. SWDet: Anchor-based object detector for solid waste detection in aerial images [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023,16:306-320.
[34] Rajkumar A, Kft C A, Sziranyi T, et al. Detecting landfills using multi-spectral satellite images and deep learning methods[J]. Proc. ICLR, 2022: 1-9.
[35] Sun X, Yin D, Qin F, et al. Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery [J]. Nature Communications, 2023,14(1):1444.
[36] Lin S F, Huang L, Liu X L, et al. A construction waste landfill dataset of two districts in Beijing, China from high resolution satellite images [J]. Scientific Data, 2024,11(1):388.
[37] Guo Z, Zeng Z, Zhang Q, et al. SCED-Net: Coupling dual-branch and encoder-decoder network for identification and delineation of municipal solid waste from VHR images [J]. IEEE Geoscience and Remote Sensing Letters, 2023,21:1-5.
[38] Lyu J, Hu Y, Ren S, et al. Extracting the tailings ponds from high spatial resolution remote sensing images by integrating a deep learning-based model [J]. Remote Sensing, 2021,13(4):743.
[39] Lotfi S, Habibi K, Koohsari M J. Integrating GIS and fuzzy logic for urban solid waste management (a case study of Sanandaj city, Iran) [J]. Pakistan Journal of Biological Sciences, 2007,10(22):4000-4007.
[40] Yang K, Zhou X N, Yan W A, et al. Landfills in Jiangsu province, China, and potential threats for public health: Leachate appraisal and spatial analysis using geographic information system and remote sensing [J]. Waste Management, 2008,28(12):2750-2757.
[41] Sener Ş, Sener, et al. Solid waste disposal site selection with GIS and AHP methodology: a case study in Senirkent-Uluborlu (Isparta) Basin, Turkey [J]. Environmental Monitoring and Assessment, 2011,173(1): 533-554.
[42] Krishna V V S, Pandey, et al. Geospatial multicriteria approach for solid waste disposal site selection in Dehradun city, India [J]. Current Science, 2017,112(3):549-559.
[43] Richter A, Ng, et al. A data driven technique applying GIS, and remote sensing to rank locations for waste disposal site expansion [J]. Resources, Conservation and Recycling, 2019,149:352-362.
[44] Abd-El Monsef H. Optimization of municipal landfill siting in the Red Sea coastal desert using geographic information system, remote sensing and an analytical hierarchy process [J]. Environmental Earth Sciences, 2015,74(3):2283-2296.
[45] Ahire V, Behera D K, Saxena M R, et al. Potential landfill site suitability study for environmental sustainability using GIS-based multi-criteria techniques for Nashik and environs [J]. Environmental Earth Sciences, 2022,81(6):178.
[46] Karimi N, Ng, et al. Integrating geographic information system network analysis and nighttime light satellite imagery to optimize landfill regionalization on a regional level [J]. Environmental Science and Pollution Research, 2022,29(54):81492-81504.
[47] Shahabi H, Keihanfard S, Ahmad B B, et al. Evaluating Boolean, AHP and WLC methods for the selection of waste landfill sites using GIS and satellite images [J]. Environmental Earth Sciences, 2014,71:4221- 4233.
[48] Sheoran S K, Parmar V. Identification of alternative landfill site using QGIS in a densely populated metropolitan area [J]. Quaestiones Geographicae, 2020,39(3):47-56.
[49] Guha B, Momtaz Z, Rahaman Z A. Estimating solid waste generation and suitability analysis of landfill sites using regression, geospatial, and remote sensing techniques in Rangpur, Bangladesh [J]. Environmental Monitoring and Assessment, 2023,195(1):1-28.
[50] Kapilan S, Elangovan K. Potential landfill site selection for solid waste disposal using GIS and multi-criteria decision analysis (MCDA) [J]. Journal of Central South University, 2018,25(3):570-585.
[51] Molla M B. Potential landfill site selection for solid waste disposal using GIS-based multi-criteria decision analysis (MCDA) in Yirgalem Town, Ethiopia [J]. Cogent Engineering, 2024,11(1): 2297486.
[52] Richter A, Ng K T W, Karimi N. A data driven technique applying GIS, and remote sensing to rank locations for waste disposal site expansion [J]. Resources, Conservation and Recycling, 2019,149: 352-362.
[53] Cadau E G, Putignano C, Aurigemma R, et al. SIMDEO: An integrated system for landfill detection and monitoring using EO data [C]//IEEE International Geoscience and Remote Sensing Symposium, 2013.
[54] Lella J, Mandla V R, Zhu X. Solid waste collection/transport optimization and vegetation land cover estimation using Geographic Information System (GIS): A case study of a proposed smart-city [J]. Sustainable Cities and Society, 2017,35:336-349.
[55] Iacoboaea C, Petrescu F. Landfill monitoring using remote sensing: a case study of Glina, Romania [J]. Waste Management & Research, 2013,31(10):1075-1080.
[56] Yan W Y, Mahendrarajah P, Shaker A, et al. Analysis of multi- temporal Landsat satellite images for monitoring land surface temperature of municipal solid waste disposal sites [J]. Environmental Monitoring and Assessment, 2014,186:8161-8173.
[57] Richter A, Kazaryan, et al. Estimation of thermal characteristics of waste disposal sites using Landsat satellite images [J]. Dokladi Na Bolgarskata Akademiya Na Naukite, 2017,70(2):253-262.
[58] Karimi N, Richter A, Ng K T W. Temporal and spatial assessment of landfill gas emission near the City of Regina landfill[C]//Canadian Society of Civil Engineering Annual Conference. Singapore: Springer Nature Singapore, 2021:145-153.
[59] Cusworth D H, Duren R M, Ayasse A K, et al. Quantifying methane emissions from United States landfills [J]. Science, 2024,383(6690): 1499-1504.
[60] Wang Y, Fang M L, Lou Z Y, et al. Methane emissions from landfills differentially underestimated worldwide [J]. Nature Sustainability, 2024,7(4):496-507.
[61] Nesser H, Nesser H, Jacob D J, et al. High-resolution US methane emissions inferred from an inversion of 2019TROPOMI satellite data: contributions from individual states, urban areas, and landfills [J]. Atmospheric Chemistry and Physics, 2024,24(8):5069-5091.
[62] Maasakkers J D, Varon D J, Elfarsdóttir A, et al. Using satellites to uncover large methane emissions from landfills [J]. Science Advances, 2022,8(32):eabn9683.
[63] Cusworth D H, Duren R M, Thorpe A K, et al. Using remote sensing to detect, validate, and quantify methane emissions from California solid waste operations [J]. Environmental Research Letters, 2020, 15(5):054012.
[64] Scarpelli T R, Cusworth D H, Duren R M, et al. Investigating major sources of methane emissions at US landfills [J]. Environmental Science & Technology, 2024,58(49):21545-21556.
PDF(529 KB)

Accesses

Citation

Detail

Sections
Recommended

/