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cs.CV2026

Vision-Language Agents for Interactive Forest Change Analysis

James Brock, Ce Zhang, Nantheera Anantrasirichai

Modern forest monitoring workflows increasingly benefit from the growing availability of high-resolution satellite imagery and advances in deep learning. Two persistent challenges…

cs.CV2026

Forest-Chat: Adapting Vision-Language Agents for Interactive Forest Change Analysis

James Brock, Ce Zhang, Nantheera Anantrasirichai

The increasing availability of high-resolution satellite imagery, together with advances in deep learning, creates new opportunities for forest monitoring workflows. Two central ch…

cs.CV2025

DGL-RSIS: Decoupling Global Spatial Context and Local Class Semantics for Training-Free Remote Sensing Image Segmentation

Boyi Li, Ce Zhang, Richard M. Timmerman +1

The emergence of vision language models (VLMs) bridges the gap between vision and language, enabling multimodal understanding beyond traditional visual-only deep learning models. H…

cs.CV2024

OAM-TCD: A globally diverse dataset of high-resolution tree cover maps

Josh Veitch-Michaelis, Andrew Cottam, Daniella Schweizer +5

Accurately quantifying tree cover is an important metric for ecosystem monitoring and for assessing progress in restored sites. Recent works have shown that deep learning-based seg…

cs.CV2024

Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving

Ce Zhang, Azim Eskandarian

LiDAR is one of the most crucial sensors for autonomous vehicle perception. However, current LiDAR-based point cloud perception algorithms lack comprehensive and rigorous LiDAR qua…