remote sensing
The acquisition of information about an object or area from a distance, typically using satellite or aerial imaging. In AI, remote sensing data can be analyzed for various applications, such as environmental monitoring and urban planning.
- CHOICE: Benchmarking the Remote Sensing Capabilities of Large Vision-Language Models
- CarbonGlobe: A Global-Scale, Multi-Decade Dataset and Benchmark for Carbon Forecasting in Forest Ecosystems
- Coupled Data and Measurement Space Dynamics for Enhanced Diffusion Posterior Sampling
- DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Response
- GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
- GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Data
- GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction
- ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression
- ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression
- L2RSI: Cross-view LiDAR-based Place Recognition for Large-scale Urban Scenes via Remote Sensing Imagery
- MONITRS: Multimodal Observations of Natural Incidents Through Remote Sensing
- Online Feedback Efficient Active Target Discovery in Partially Observable Environments
- Physics-informed Neural Operator for Pansharpening
- Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models
- RSCC: A Large-Scale Remote Sensing Change Caption Dataset for Disaster Events
- RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
- TreeFinder: A US-Scale Benchmark Dataset for Individual Tree Mortality Monitoring Using High-Resolution Aerial Imagery