3 papers
cs.CV2026
USAI-Quant: A Quantitative Reasoning Benchmark for Vision-Language Models in Built Environments
Dongdong Wang, Qingqi Song, Yuzhou Chen +3
Large vision-language models (VLMs) have emerged as a powerful paradigm for urban and spatial AI. However, current state-of-the-art large VLMs still struggle with quantitative reas…
cs.CV2026
Gradient-Guided Decoupled Adaptation for Geospatial Vision-Language Models
Dongdong Wang, Deepak Balakrishnan, Ravi Srinivasan +1
Existing geospatial vision-language models (Geo-VLMs) typically optimize diverse geospatial tasks through a unified multi-task adaptation paradigm without explicitly accounting for…
cs.CL2026
Built Environment Reasoning from Remote Sensing Imagery Using Large Vision--Language Models
Dongdong Wang, Deepak Balakrishnan, Ravi Srinivasan +1
This work investigates the use of large language models (LLMs) for tasks in smart cities. The core idea is to leverage remote sensing imagery to characterize the built environment,…