GS-QA is a new benchmark of 2,800 QA pairs on 28 templates using OSM and Wikipedia data to evaluate LLMs on spatial predicates, multi-source reasoning, and diverse answer types including distances and counts.
Geogpt: Understanding and processing geospatial tasks through an autonomous gpt.arXiv preprint arXiv:2307.07930
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The paper analyzes CPU bottlenecks in agentic AI serving, selects representative workloads, and demonstrates that CPU-aware scheduling optimizations COMB and MAS can reduce P50 latency by up to 1.7x and total latency by up to 2.49x on two hardware systems.
SAM achieves ~58% accuracy delineating field boundaries from SkySat imagery without training, with gains from multi-date inputs and varied sizes, establishing proof-of-concept for data-scarce agriculture mapping.
citing papers explorer
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GS-QA: A Benchmark for Geospatial Question Answering
GS-QA is a new benchmark of 2,800 QA pairs on 28 templates using OSM and Wikipedia data to evaluate LLMs on spatial predicates, multi-source reasoning, and diverse answer types including distances and counts.
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Towards Understanding, Analyzing, and Optimizing Agentic AI Execution: A CPU-Centric Perspective
The paper analyzes CPU bottlenecks in agentic AI serving, selects representative workloads, and demonstrates that CPU-aware scheduling optimizations COMB and MAS can reduce P50 latency by up to 1.7x and total latency by up to 2.49x on two hardware systems.
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Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels
SAM achieves ~58% accuracy delineating field boundaries from SkySat imagery without training, with gains from multi-date inputs and varied sizes, establishing proof-of-concept for data-scarce agriculture mapping.