Starred repositories
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 …
The Clay Foundation Model - An open source AI model and interface for Earth
The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that sho…
Towards Open Vocabulary Semantic Segmentation In Remote Sensing.
Get Youtube data such as videos, playlists, channels, video information & comments, related videos, up next video, and more!
Python-based scraper to collect Baidu heatmap data and simulate mobile signaling grids for urban flow analysis.
基于深度学习的地理位置兴趣点（Point of Interest）匹配解决方案。能够智能匹配来自不同地图服务商（百度、高德）的POI数据，通过多模态特征融合和大语言模型增强技术，实现高精度的POI实体对齐。The system intelligently matches POI data from different map service providers (such as Baidu …
本研究聚焦城市动态系统下的文旅资源优化，以南京景点为对象，通过多源数据融合挖掘景点运行规律与游客情感特征。研究整合三类核心数据：含用户评论与空间位置的微博签到数据、含分类标签与地理坐标的景点 POI 数据、含天气类型与气温的南京气象数据，经坐标统一、空间关联等预处理形成三维数据集。 分析采用情感分析、K-means 聚类、LDA 主题模型及统计检验等方法，将 119 个南京景点聚为 6 类，…
A crawler code for extracting user check-in data under a certain city topic on the mobile version of Weibo, mainly used for POI recommendation.
网络舆情态势及情感多维特征分析与可视化，bert用于微博情感分类，geopandas用于绘制情绪地图。项目还提供了轻量级的微博签到爬取，带有坐标。
PDFM Embeddings: location-based vectors for geo-spatial analysis.
Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks
InstructSAM: A Training-Free Framework for Instruction-Oriented Remote Sensing Object Recognition (NeurIPS 2025)
Grounded SAM 2: Ground and Track Anything in Videos with Grounding DINO, Florence-2 and SAM 2
Reference PyTorch implementation and models for DINOv3
Restor's ML pipeline for tree crown mapping in aerial images
Explore and interpret large embeddings in your browser with interactive visualization! 📍
Embedding Atlas is a tool that provides interactive visualizations for large embeddings. It allows you to visualize, cross-filter, and search embeddings and metadata.
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