ArticleNature communications2023
Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- A Multi-Modal Remote Sensing Image Collaborative Fusion Network for Construction and Demolition Waste Extraction in Urban Areas.Sensors (Basel, Switzerland) · 2026Article
- Prioritizing dumpsite risks and interventions: an overview and framework for action with a focus on LMICs.Environmental monitoring and assessment · 2025Review
- Urban fabric decoded: High-precision building material identification via deep learning and remote sensing.Environmental science and ecotechnology · 2025Article
- Data-driven approaches linking wastewater and source estimation hazardous waste for environmental management.Nature communications · 2024Article
- Enhancing Earth data analysis in 5G satellite networks: A novel lightweight approach integrating improved deep learning.Heliyon · 2024Article
- Revealing influencing factors on global waste distribution via deep-learning based dumpsite detection from satellite imagery.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advancement of global civilisation, monitoring and managing dumpsites have become essential parts of environmental governance in various countries. Dumpsite locations are difficult to obtain in a timely manner by local government agencies and environmental groups. The World Bank shows that governments need to spend massive labour and economic costs to collect illegal dumpsites to implement management. Here we show that applying novel deep convolutional networks to high-resolution satellite images can provide an effective, efficient, and low-cost method to detect dumpsites. In sampled areas of 28 cities around the world, our model detects nearly 1000 dumpsites that appeared around 2021. This approach reduces the investigation time by more than 96.8% compared with the manual method. With this novel and powerful methodology, it is now capable of analysing the relationship between dumpsites and various social attributes on a global scale, temporally and spatially.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.