Roads are expanding faster than at any point in human history, and many of them don't appear on maps. The ghost roads study in 2024 used ~7,000 hours of volunteer effort to hand-map roads across the tropical Asia-Pacific, finding 3--6.6 times more roads than exist in current global road datasets.
Road building almost always preceded local forest loss, making unmapped roads one of the best early warning signals we have for deforestation. Mapping them quickly and globally is therefore a conservation priority and not just a "cartographic tidyup"!
The current SOTA detects roads directly from Sentinel imagery with CNN segmentation models, such as monitoring road development in the Congo Basin, mapping roads in the Brazilian Amazon, and the ghost roads team's own remote road mapping with AI. The 10m resolution of Sentinel does a surprisingly good job, esp. for new roads where there's an initial canopy opening. It's also free and global.
This project would investigate whether the TESSERA foundation model can be better/faster than raw Sentinel imagery. Tessera compresses a full year of Sentinel-1 and Sentinel-2 observations into a 128-dimensional embedding for every 10m pixel on the planet (easily accessible via GeoTessera Python library released for geospatial embeddings), so a road classifier also gets the radar signal through cloud and a year of temporal dynamics rather than a single optical scene.
That temporal signal might also allow us to separate paved from unpaved surfaces, and to spot informal logging tracks that only appear seasonally. Road detection is already one of the downstream benchmarks in the Tessera v2 evaluation suite (via the CityRep urban representation benchmark), so there is a baseline to start from.
An interesting twist with roads is that they are not independent pixels. Almost every road connects to another road! A per-pixel classifier throws that structural prior away, but a generative approach could exploit it by starting from a known road network (e.g. OpenStreetMap) and tracing outwards into the unknown. Exactly which ML technique fits best is an open question the project would settle, but candidates include:
- Connectivity-aware segmentation by adding topology-preserving losses to a conventional segmentation head over embedding patches so that predicted roads form connected networks rather than a speckle.
- Iterative tracing in the style of RoadTracer, where an agent walks outwards from known road seeds and decides at each step whether the road continues through embedding space.
- Generative graph completion, treating the mapped network as a partial graph and training a model to propose the missing edges, sampling multiple plausible completions to get uncertainty over where ghost roads might lurk.
- Temporal change detection, differencing yearly Tessera embeddings to catch new canopy openings as roads are actively constructed.
Validation can be done since the ghost roads team's hand-digitised dataset gives us high-quality labels for where roads actually are across the Asia-Pacific tropics, and beyond what OpenStreetMap coverage would suggest for those years. We have Tessera embeddings from 2017-2025.
1 See also
Andres Zuñiga-Gonzalez has done prior work on road mapping with OSM labels over Tessera embeddings; see his weeknotes on detecting new roads and categorising OSM road features, and his osm-rasterizer tool for generating training rasters from OSM features.
The Global habitat maps from Tessera embeddings work covers the broader classifier-on-embeddings methods we're developing as part of the wider TESSERA programme.