54 citations · 106 across the 19 of their papers we have counts for
20 papers
Anaximander: Interactively Running Geospatial Deep Learning Models on Any Compute Backend
Satej S. Soman, Akram Zaytar, Girmaw A. Tadesse +5
Applying deep learning models to satellite imagery from within geographic information systems (GIS) remains high-friction for remote sensing practitioners. Models arrive in incompa…
HASTE: A Platform for Rapid Post-Disaster Building Damage Assessment
Caleb Robinson, Anthony Ortiz, Simone Fobi Nsutezo +8
When a large disaster strikes, responders need a map of which buildings are damaged within hours. The models that do well on public benchmarks assume matched before-and-after image…
TukaBench: A Culturally Grounded Jailbreak Benchmark for African Languages
Victor Akinode, Senyu Li, Wassim Hamidouche +3
Safety evaluation of Large Language Models (LLMs) remains heavily English-centric, leaving Low-Resource Languages (LRLs), particularly African ones, critically underexplored. We in…
Project SPARROW and the Future of Conservation Technology
Juan M. Lavista Ferres, Carl Chalmers, Bruno Demuro Segundo +14
Global biodiversity is declining at unprecedented rates, yet the tools available to monitor and protect ecosystems remain limited by constraints in power, connectivity, and accessi…
The first global agricultural field boundary map at 10m resolution
Caleb Robinson, Gedeon Muhawenayo, Subash Khanal +9
The agricultural field is the natural unit at which crops are planted, managed, regulated, and reported, yet most global remote-sensing products for agriculture are only available…
WATCH: Wide-Area Archaeological Site Tracking for Change Detection
Girmaw Abebe Tadesse, Titien Bartette, Andrew Hassanali +7
Monitoring archaeological sites at scale is vital for protecting cultural heritage, yet pinpointing when disturbances occur remains difficult because visual cues are subtle and gro…
GeoAI Agency Primitives
Akram Zaytar, Rohan Sawahn, Caleb Robinson +5
We present ongoing research on agency primitives for GeoAI assistants -- core capabilities that connect Foundation models to the artifact-centric, human-in-the-loop workflows where…
XMENTOR: A Rank-Aware Aggregation Approach for Human-Centered Explainable AI in Just-in-Time Software Defect Prediction
Saumendu Roy, Banani Roy, Chanchal Roy +1
Machine learning (ML)-based defect prediction models can improve software quality. However, their opaque reasoning creates an HCI challenge because developers struggle to trust mod…
Satellite-Based Detection of Looted Archaeological Sites Using Machine Learning
Girmaw Abebe Tadesse, Titien Bartette, Andrew Hassanali +7
Looting at archaeological sites poses a severe risk to cultural heritage, yet monitoring thousands of remote locations remains operationally difficult. We present a scalable and sa…
BYOL: Bring Your Own Language Into LLMs
Syed Waqas Zamir, Wassim Hamidouche, Boulbaba Ben Amor +3
Large Language Models (LLMs) exhibit strong multilingual capabilities, yet remain fundamentally constrained by the severe imbalance in global language resources. While over 7,000 l…
TEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery
Tammy Glazer, Gilles Q. Hacheme, Akram Zaytar +9
We present TEMPO, a global, temporally resolved dataset of building density and height derived from high-resolution satellite imagery using deep learning models. We pair building f…
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…
AI Diffusion in Low Resource Language Countries
Amit Misra, Syed Waqas Zamir, Wassim Hamidouche +2
Artificial intelligence (AI) is diffusing globally at unprecedented speed, but adoption remains uneven. Frontier Large Language Models (LLMs) are known to perform poorly on low-res…
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,…
Satellite Data Shows Resilience of Tigrayan Farmers in Crop Cultivation During Civil War
Hannah Kerner, Catherine Nakalembe, Benjamin Yeh +4
The Tigray War was an armed conflict that took place primarily in the Tigray region of northern Ethiopia from November 3, 2020 to November 2, 2022. Given the importance of agricult…
How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?
Hannah Kerner, Catherine Nakalembe, Adam Yang +4
Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa,…
Improve State-Level Wheat Yield Forecasts in Kazakhstan on GEOGLAM's EO Data by Leveraging A Simple Spatial-Aware Technique
Anh Nhat Nhu, Ritvik Sahajpal, Christina Justice +1
Accurate yield forecasting is essential for making informed policies and long-term decisions for food security. Earth Observation (EO) data and machine learning algorithms play a k…
Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization
Hannah Kerner, Ritvik Sahajpal, Sergii Skakun +5
Crop type classification using satellite observations is an important tool for providing insights about planted area and enabling estimates of crop condition and yield, especially…
Rapid Response Crop Maps in Data Sparse Regions
Hannah Kerner, Gabriel Tseng, Inbal Becker-Reshef +5
Spatial information on cropland distribution, often called cropland or crop maps, are critical inputs for a wide range of agriculture and food security analyses and decisions. Howe…
Field-Level Crop Type Classification with k Nearest Neighbors: A Baseline for a New Kenya Smallholder Dataset
Hannah Kerner, Catherine Nakalembe, Inbal Becker-Reshef
Accurate crop type maps provide critical information for ensuring food security, yet there has been limited research on crop type classification for smallholder agriculture, partic…