license: apache-2.0
datasets:
- vikki23/LAS
Model Card for LandSegmenter
This repo provides the weights for LandSegmenter, a task-specific Foundation Model (FM) for Land Use and Land Cover (LULC) mapping, characterized by high flexibility in both inputs (multi-band, multi-resolution imagery) and outputs (customizable category definitions), supporting both zero-shot inference and fine-tuning.
Model Details
Model Description
LandSegmenter is the first LULC FM trained with LAS, designed with high flexibility to handle diverse input modalities and customizable category settings. It builds on SAM2's backbone for robust multi-scale spatial representation, further enhanced by multispectral features from DOFA and high-frequency components for refined structural details. A text-based prompter derived from GeoRSCLIP, which takes class names as inputs, further strengthens its semantic understanding, enabling concept-aware and adaptable segmentation across heterogeneous data sources
- Developed by: Chenying Liu, Wei Huang, Xiao Xiang Zhu
- Funded by: Munich Center for Machine Learning (MCML)
Model Sources
Uses
Please refer to the GitHub repository for implementation details.
Model Card Authors
Chenying Liu