Mapping Roads in the Brazilian Amazon with Artificial Intelligence and Sentinel-2
Open accessAbstract
This study presents our efforts to automate the detection of unofficial roads (herein, roads) in the Brazilian Amazon using artificial intelligence (AI). In this region, roads are built by loggers, goldminers, and unauthorized land settlements from existing official roads, expanding over pristine forests and leading to new deforestation and fire hotspots. Previous research used visual interpretation, hand digitization, and vector editing techniques to create a thorough Amazon Road Dataset (ARD) from Landsat imagery. The ARD allowed assessment of the road dynamics and impacts on deforestation, landscape fragmentation, and fires and supported several scientific and societal applications. This research used the existing ARD to train and model a modified U-Net algorithm to detect rural roads in the Brazilian Amazon using Sentinel-2 imagery from 2020 in the Azure Planetary Computer platform. Moreover, we implemented a post-AI detection protocol to connect and vectorize the U-Net road detected to create a new ARD. We estimated the recall and precision accuracy using an independent ARD dataset, obtaining 65% and 71%, respectively. Visual interpretation of the road detected with the AI algorithm suggests that the accuracy is underestimated. The reference dataset does not include all roads that the AI algorithm can detect in the Sentinel-2 imagery. We found an astonishing footprint of roads in the Brazilian Legal Amazon, with 3.46 million km of roads mapped in 2020. Most roads are in private lands (~55%) and 25% are in open public lands under land grabbing pressure. The roads are also expanding over forested areas with 41% cut or within 10 km from the roads, leaving 59% of the 3.1 million km2 of the remaining original forest roadless. Our AI and post-AI models fully automated road detection in rural areas of the Brazilian Amazon, making it possible to operationalize road monitoring. We are using the AI road map to understand better rural roads’ impact on new deforestation, fires, and landscape fragmentation and to support societal and policy applications for forest conservation and regional planning.
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Smaller areas with high transition potential were found scattered within deforestation frontiers, where most forests have been carved by roads.
“Smaller areas with high transition potential were found scattered within deforestation frontiers, where most forests have been carved by roads50,51.”
From Critical transitions in the Amazon forest system (2024) · cites this paper as [50]
The Discussion reports that roads cut through or lie within 10 km of some remaining Amazon forest, supporting the road-impact part of the cited clause. It does not establish that most forests specifically within deforestation frontiers have been carved by roads; across the remaining forest it reports 41% affected and 59% roadless. The finding about smaller areas with high transition potential appears to be the citing authors’ own result, not a finding attributed to this source. (LLM, conf=0.84)
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- Authors
- Botelho, J., Costa, S. C. P., Ribeiro, J. G., & Souza, C. M.
- Affiliations
- Imazon (BR)
- IMAZON-Amazon Institute of People and the Environment, Belém 66055-200, PA, Brazil
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2022-07-28
- Volume
- 14
- Issue
- 15
- Pages
- 3625
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W4288722736
- Semantic Scholar
- 0f589ab8e6c9af4c7d14d1881f8092e17f0f04f0
- Cited by
- 43
- References
- 39
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 8 · 2023: 11 · 2022: 2
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Climate Land Use Alliance (G-2008-57051, #009/2020)
- Fundo Vale (G-2008-57051, #009/2020)
- Topics
- Physical Sciences › Engineering › Ocean Engineering › Automated Road and Building Extraction
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- road detection, Brazilian Amazon, U-Net, Sentinel-2, roadmapping, deforestation, landscape fragmentation, forest fires, land grabbing, forest conservation, road monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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