GeoVista introduces a planning-driven active perception framework with global exploration plans, branch-wise local inspection, and explicit evidence tracking to achieve state-of-the-art results on ultra-high-resolution remote sensing benchmarks.
RSGPT: A remote sensing vision language model and benchmark
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
background 1
baseline 1
citation-polarity summary
years
2026 7verdicts
UNVERDICTED 7representative citing papers
ScaleEarth conditions remote sensing VLMs on continuous GSD via CS-HLoRA and a visual GSD predictor, creating a closed training loop with GeoScale-VQA to achieve SOTA on Earth observation benchmarks.
Geo2Sound generates geographically realistic soundscapes from satellite imagery via geospatial attribute modeling, semantic hypothesis expansion, and geo-acoustic alignment, achieving SOTA FAD of 1.765 on a new 20k-pair benchmark.
ChangeQuery is a new multimodal framework for semantic disaster change analysis that combines optical and SAR data with a custom dataset and annotation pipeline to support interactive damage assessment.
RSICCLLM introduces a post-training framework with RSICI dataset, difference-aware supervised fine-tuning, and dual-negative preference optimization that claims to outperform much larger models on remote sensing image change captioning.
OmniCD proposes a multimodal semantic-guided framework for remote sensing change detection supporting binary to zero-shot tasks, plus the RSITCD dataset, with claimed SOTA performance.
WildfireVLM integrates YOLOv12 object detection on satellite imagery with multimodal LLMs to detect wildfires and produce contextual risk assessments and response recommendations.
citing papers explorer
-
GeoVista: Visually Grounded Active Perception for Ultra-High-Resolution Remote Sensing Understanding
GeoVista introduces a planning-driven active perception framework with global exploration plans, branch-wise local inspection, and explicit evidence tracking to achieve state-of-the-art results on ultra-high-resolution remote sensing benchmarks.
-
Beyond GSD-as-Token: Continuous Scale Conditioning for Remote Sensing VLMs
ScaleEarth conditions remote sensing VLMs on continuous GSD via CS-HLoRA and a visual GSD predictor, creating a closed training loop with GeoScale-VQA to achieve SOTA on Earth observation benchmarks.
-
Geo2Sound: A Scalable Geo-Aligned Framework for Soundscape Generation from Satellite Imagery
Geo2Sound generates geographically realistic soundscapes from satellite imagery via geospatial attribute modeling, semantic hypothesis expansion, and geo-acoustic alignment, achieving SOTA FAD of 1.765 on a new 20k-pair benchmark.
-
ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understanding
ChangeQuery is a new multimodal framework for semantic disaster change analysis that combines optical and SAR data with a custom dataset and annotation pipeline to support interactive damage assessment.
-
RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning
RSICCLLM introduces a post-training framework with RSICI dataset, difference-aware supervised fine-tuning, and dual-negative preference optimization that claims to outperform much larger models on remote sensing image change captioning.
-
OmniCD: A Foundational Framework for Remote Sensing Image Change Detection Guided by Multimodal Semantics
OmniCD proposes a multimodal semantic-guided framework for remote sensing change detection supporting binary to zero-shot tasks, plus the RSITCD dataset, with claimed SOTA performance.
-
WildfireVLM: AI-powered Analysis for Early Wildfire Detection and Risk Assessment Using Satellite Imagery
WildfireVLM integrates YOLOv12 object detection on satellite imagery with multimodal LLMs to detect wildfires and produce contextual risk assessments and response recommendations.