city-roads is a browser-based single-page application that fetches OpenStreetMap road data and renders every road in a chosen city as WebGL line art. Users search for a city; the app geocodes the query via Nominatim, suggests administrative boundaries, and then loads data either from a prebuilt protobuf cache hosted on CloudFront/S3 or from the Overpass API. The downloaded OSM nodes and ways are parsed into a Grid object, projected into 2D local coordinates with a Mercator projection, and pushed into a w-gl WireCollection for fast canvas rendering. The UI supports changing road, background, and label colors, editing the city label, saving PNG and SVG exports, and generating Zazzle mug links. A console API exposed as window.scene and window.Query lets advanced users run arbitrary Overpass queries and compose multiple city layers. Architecturally it is a Vue 3/Vite frontend with a clean separation between small lib modules (request, progress, grid, query builders, exporters) and UI components. There is no backend; all data comes from OSM and Nominatim public services plus a static cache. The repository is primarily an application rather than a data-research project, but its lib layer contains genuinely reusable geospatial and Overpass utilities. The codebase is compact, mostly readable, and handles real-world failure modes such as Overpass mirror outages, large downloads, cancellation, and high-resolution exports. Notable weaknesses are the absence of automated tests and CI, leftover Vue CLI configuration, outdated dependencies (Vite 2, Vue 3.2), a debugger statement in App.vue, and a broken custom-projector branch in LoadOptions. Overall it is a solid, functional visualization app with several modular components that could be extracted for other OSM or WebGL projects.
This repository defines the quantized-mesh-1.0 terrain format, a specification for encoding and streaming terrain tiles in a multi-resolution quadtree pyramid. It includes detailed documentation of the binary tile structure (header, vertex data, index data, edge indices, and extensions) and the JSON manifest format (layer.json) with accompanying JSON schemas for validation. The format is used by CesiumJS for 3D terrain rendering and enables efficient sharing of terrain data. While the repository does not contain processing code, it provides a complete and reusable specification for producers and consumers of quantized-mesh tiles, including the schemas for layer manifests and availability rectangles.
TouchTerrain is a web application and standalone tool that converts digital elevation models (DEMs) into 3D-printable terrain models (STL/OBJ). It uses Google Earth Engine as the primary DEM source, with support for local GeoTIFF files. The architecture includes a Flask web server with a frontend using Google Maps and Three.js for preview, a standalone Python script for batch processing, and Jupyter notebooks for interactive use. The core processing pipeline fetches DEM data, resamples it, tiles it, generates mesh geometry with optional walls and bottom, and bundles the output as a ZIP file. The repository also includes geospatial utilities (coordinate conversion, KML parsing, GPX path embossing) and raster cleaning functions. While the codebase is functional and well-documented, it shows signs of technical drift: multiprocessing is broken, JavaScript libraries are outdated (Three.js r71), and there is commented-out code. The project is actively maintained with recent commits and issue triage.
This repository provides access to Microsoft's global building footprint dataset (1.4B buildings) derived from Bing Maps satellite imagery. The core deliverable is a massive, publicly hosted dataset of building polygons in GeoJSONL format, with periodic updates. The repository itself contains only documentation, a Jupyter notebook demonstrating data download and spatial filtering, and a few utility scripts for converting compressed GeoJSONL files to GIS-friendly formats. There is no transformation pipeline or model training code present; the actual building extraction is done externally. The code is simple, well-documented, and parameterized for reuse, but lacks dependency management and environment definitions. The primary value is the dataset itself, with the scripts serving as examples for accessing and processing the data.
This repository is a curated collection of over 30 independent Streamlit in Snowflake (SiS) demo applications, each self-contained in its own folder. The demos span a wide range of use cases including business intelligence dashboards, LLM-powered chatbots (Cortex, DeepSeek-R1, RAG), geospatial H3 visualizations, custom UI components (AgGrid, ECharts, Slickgrid), CI/CD pipelines with Snowflake CLI, and embedding SiS into external portals via Next.js. Each app includes a `streamlit_app.py` entry point, a `data/creation_script.sql` for Snowflake object setup, and sample data. The code is educational, heavily commented, and demonstrates patterns like caching, dynamic page navigation, and local/cloud-aware connections. While the apps are not production-grade (some hardcoded values, duplication), they serve as excellent reference implementations for building SiS applications.
This repository provides unofficial precompiled binary wheels for a comprehensive set of geospatial Python libraries (e.g., GDAL, Fiona, Shapely, rasterio) on Windows, including Windows ARM64. The repository itself contains no source code; it functions purely as a distribution channel and release archive. The wheel builds incorporate numerous statically or dynamically linked native libraries (GEOS, PROJ, HDF5, etc.) and target recent Python versions (3.12–3.15). Build environment details are documented in the README (Visual Studio 2022, Windows Dev Kit, Rust). This is not a reusable software project but a convenient binary distribution service for the geospatial Python ecosystem on Windows.