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Starred repositories
Landslide susceptibility mapping using Machine Learning - A Danish case study
This repo aims to develope a foundational model using california CDL (Cropland Data Layer) for different downstream tasks.
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
I employ an advanced approach combining Google Earth Engine (GEE) and data from the MODIS satellite to gather comprehensive remote sensing data. This data is then analyzed using cutting-edge Machin…
Data description and baseline code for LandSlide4Sense 2022 competition
MTEB: Massive Text Embedding Benchmark
[NeurIPS 2025] DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Response
This is the implement of the paper "DynamicVis: An Efficient and General Visual Foundation Model for Remote Sensing Image Understanding"
AI Framework for Remote Sensing Image Analysis using RAG - 88%+ accuracy, multi-modal queries, ChatGPT-like interface
A Python toolkit for fine-tuning Geospatial Foundation Models (GFMs).
gpt-oss-120b and gpt-oss-20b are two open-weight language models by OpenAI
Learning without Forgetting for Vision-Language Models (TPAMI 2025)
Awsome of VLM-CL. Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting
The official web site of the OGC SensorThings API standard specification.
TESSERA is a foundation model that can process time-series satellite imagery for applications such as land classification and canopy height prediction. Developed at the University of Cambridge, it …
Python bindings for H3, a hierarchical hexagonal geospatial indexing system
https://ogcapi.ogc.org/dggs
主要记录大语言大模型（LLMs） 算法（应用）工程师相关的知识及面试题
On the Theoretical Limitations of Embedding-Based Retrieval
A Survey on Vision-Language Geo-Foundation Models (VLGFMs)
A curated list of foundation models for vision and language tasks
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and cont…
Implementation of all RAG techniques in a simpler way
Implementation of all RAG techniques in a simpler way（以简单的方式实现所有 RAG 技术）
Welcome to the documentation for the datasets supporting the Potemkin Benchmark.