Gilles Hacheme

shocklab/Literature-survey-GeoAI-Africa: Initial Submission Release

Ian A. Ocholla, Aisha Alaagib, Gilles Hacheme, Yawogan Jean Eudes Gbodjo et autres

Study records for the review manuscript: "GeoAI for Africa: A Survey of Data Gaps, Methods, and the Path Toward Inclusive Geospatial Intelligence"

Afrique du Sud, gb, fr, fi (code pays fourni par la source)

shocklab/Literature-survey-GeoAI-Africa: Initial Submission Release

Ian A. Ocholla, Aisha Alaagib, Gilles Hacheme, Yawogan Jean Eudes Gbodjo et autres

Study records for the review manuscript: "GeoAI for Africa: A Survey of Data Gaps, Methods, and the Path Toward Inclusive Geospatial Intelligence"

Afrique du Sud, gb, fr, fi (code pays fourni par la source)

Survey Protocol Cards for Crop Maps

Akram Zaytar, Girmaw Abebe Tadesse, Caleb Robinson, Shabarinath Nair et autres

Crop type maps underpin food security decisions yet their accuracy depends on label quality, which in turn depends on survey design choices made under tight budgets. Survey planners must allocate limited resources across GPS devices, stratification strategies, sample size, worker training, and …

GeoAI Agency Primitives

Akram Zaytar, Rohan Sawahn, Caleb Robinson, Gilles Hacheme et autres

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 GIS practitioners actually work. Despite advances in satellite image captioning, visual question answering, and promptable segmentation, these capabilities have …

Survey Protocol Cards for Crop Maps

Akram Zaytar, Girmaw A. Tadesse, Caleb Robinson, Shabarinath S Nair et autres

Crop type maps underpin food security decisions yet their accuracy depends on label quality, which in turn depends on survey design choices made under tight budgets. Survey planners must allocate limited resources across GPS devices, stratification strategies, sample size, worker training, and …

us, mx, be, at (code pays fourni par la source)

Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation

Burak Ekim, Girmaw Abebe Tadesse, Caleb Robinson, Gilles Hacheme et autres

Training robust deep learning models is crucial in Earth Observation, where globally deployed models often face distribution shifts that degrade performance, especially in low-data regions. Out-of-distribution (OOD) detection addresses this by identifying inputs that deviate from indistribution (ID) data. However, existing methods …

de, gb (code pays fourni par la source)

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data

Akram Zaytar, Caleb Robinson, Girmaw Abebe Tadesse, Tammy Glazer et autres

Training deep learning models on petabyte-scale Earth observation (EO) data requires separating compute resources from data storage. However, standard PyTorch data loaders cannot keep modern GPUs utilized when streaming GeoTIFF files directly from cloud storage. In this work, we benchmark GeoTIFF loading …

GeoVision Labeler: Zero-Shot Geospatial Classification with Vision and Language Models

Gilles Hacheme, Girmaw Abebe Tadesse, Caleb Robinson, Akram Zaytar et autres

Classifying geospatial imagery remains a major bottleneck for applications such as disaster response and land-use monitoring-particularly in regions where annotated data is scarce or unavailable. Existing tools (e.g., RS-CLIP) that claim zero-shot classification capabilities for satellite imagery nonetheless rely on task-specific pretraining …

Expanding smallholder irrigation in central Kenya demonstrates the importance of protecting grassland landscapes

Gilles Hacheme, Stephen Andrew Wood, Renatus Magesa, Girmaw Abebe Tadesse et autres

Abstract The rapid expansion of agriculture in Kenya, driven by the country’s growing population, poses critical environmental challenges such as climate change and biodiversity loss. While deforestation has received much attention, the equally vital grasslands are under significant threat. Monitoring efforts have …

us, gb, Kenya (code pays fourni par la source)

Enhancing Food Security With High-Quality Land-Use and Land-Cover Maps: A Local Model Approach

Girmaw Abebe Tadesse, Caleb Robinson, Esther N. Maina, Joshua Nyakundi et autres

In 2023, 58.0% of the African population experienced moderate to severe food insecurity, with 21.6% facing severe food insecurity. Land-use and land-cover maps enable informed resource management, urban planning, environment monitoring to enhance food security. The development of global landcover maps has …

us, Kenya (code pays fourni par la source)

Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation

Burak Ekim, Girmaw Abebe Tadesse, Caleb Robinson, Gilles Hacheme et autres

Training robust deep learning models is crucial in Earth Observation, where globally deployed models often face distribution shifts that degrade performance, especially in low-data regions. Out-of-distribution (OOD) detection addresses this by identifying inputs that deviate from in-distribution (ID) data. However, existing methods …

Local vs. Global: Local Land-Use and Land-Cover Models Deliver Higher Quality Maps

Girmaw Abebe Tadesse, Caleb Robinson, Esther N. Maina, Joshua Nyakundi et autres

In 2023, 58.0% of the African population experienced moderate to severe food insecurity, with 21.6% facing severe food insecurity. Land-use and land-cover maps provide crucial insights for addressing food insecurity by improving agricultural efforts, including mapping and monitoring crop types and estimating …