Mapping illegal waste dumping sites with neural-network classification of satellite imagery
Public health and habitat quality are crucial goals of urban planning. In recent years, the severe social and environmental impact of illegal waste dumping sites has made them one of the most serious problems faced by cities in the Global South, in a context of scarce information available for decis...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
16-10-2021
|
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Public health and habitat quality are crucial goals of urban planning. In
recent years, the severe social and environmental impact of illegal waste
dumping sites has made them one of the most serious problems faced by cities in
the Global South, in a context of scarce information available for decision
making. To help identify the location of dumping sites and track their
evolution over time we adopt a data-driven model from the machine learning
domain, analyzing satellite images. This allows us to take advantage of the
increasing availability of geo-spatial open-data, high-resolution satellite
imagery, and open source tools to train machine learning algorithms with a
small set of known waste dumping sites in Buenos Aires, and then predict the
location of other sites over vast areas at high speed and low cost. This case
study shows the results of a collaboration between Dymaxion Labs and
Fundaci\'on Bunge y Born to harness this technique in order to create a
comprehensive map of potential locations of illegal waste dumping sites in the
region. |
|---|---|
| DOI: | 10.48550/arxiv.2110.08599 |