54 citations · 107 across the 10 of their papers we have counts for
10 papers
No One Knows the State of the Art in Geospatial Foundation Models
Isaac Corley, Nils Lehmann, Caleb Robinson +6
Geospatial foundation models (GFMs) have been proposed as generalizable backbones for disaster response, land-cover mapping, food-security monitoring, and other high-stakes Earth-o…
Cropland Mapping using Geospatial Embeddings
Ivan Zvonkov, Gabriel Tseng, Inbal Becker-Reshef +1
Accurate and up-to-date land cover maps are essential for understanding land use change, a key driver of climate change. Geospatial embeddings offer a more efficient and accessible…
Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal
Christina Butsko, Kristof Van Tricht, Gabriel Tseng +6
The increasing availability of geospatial foundation models has the potential to transform remote sensing applications such as land cover classification, environmental monitoring,…
DataS^3: Dataset Subset Selection for Specialization
Neha Hulkund, Alaa Maalouf, Levi Cai +15
In many real-world machine learning (ML) applications (e.g. detecting broken bones in x-ray images, detecting species in camera traps), in practice models need to perform well on s…
Galileo: Learning Global & Local Features of Many Remote Sensing Modalities
Gabriel Tseng, Anthony Fuller, Marlena Reil +7
We introduce a highly multimodal transformer to represent many remote sensing modalities - multispectral optical, synthetic aperture radar, elevation, weather, pseudo-labels, and m…
Classification Drives Geographic Bias in Street Scene Segmentation
Rahul Nair, Gabriel Tseng, Esther Rolf +2
Previous studies showed that image datasets lacking geographic diversity can lead to biased performance in models trained on them. While earlier work studied general-purpose image…