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3D Terrain Viewer Click anywhere on a map to load satellite-textured 3D terrain (Three.js). Rotate, zoom, and switch to contour mode to explore landscapes in detail. Great open source tool for geographic visualization (by W3Reality): w3reality.github .io/three-geo/examples/geo-viewer/io

68,323 次观看 • 10 个月前 •via X (Twitter)

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GeoLibre v2.0.0 is here! GeoLibre is a free and open-source geospatial platform that runs everywhere: as a native desktop app, in the browser, on Android, and embedded right inside Jupyter notebooks. It brings modern web mapping, cloud-native data formats, and a full processing toolbox together in one place, all built on MapLibre and with no proprietary lock-in. Our first major release adds a true 3D globe, takes mapping beyond Earth to Mars and the Moon, lets styles round-trip with QGIS, and turns loaded vector layers into editable, save-back-to-source data. What's new in v2.0.0 - Planetary mapping: explore Mars, the Moon, and other bodies with the OpenPlanetaryMap basemaps, a per-project ellipsoid, and a planet switcher right in the Layers panel. - CesiumJS 3D globe: switch any map pane to a photorealistic 3D globe that stays camera-synced with your 2D maps and mirrors the layer stack. - True 3D data: render vector layers with Z coordinates, load TIN/MultiPatch 3D shapefiles, and display KML/KMZ Collada (.dae) 3D models. - Symbology interchange: import and export vector styling as OGC SLD, QGIS QML, and Mapbox GL style JSON, so styles round-trip between GeoLibre, QGIS, and the Mapbox/MapLibre ecosystem. - Editable source layers: edit vector layers and write the changes back to their source, including GeoPackage and GeoJSON files and PostGIS database tables. - Weather and sky: a new Weather menu with live cloud and precipitation radar overlays (RainViewer), plus a Google Earth-style sun position simulation for realistic lighting. - Terrain and lighting: double-click the terrain control to set vertical exaggeration, and view any scene in true 3D relief. - Smarter data import: bring in CSV without coordinates as an attribute table, split GPX track points and route points into separate layers, and load macOS-zipped and projected-CRS shapefiles. - Raster in the browser: build normalized-difference indices for any HTTP COG and extract COG/WMS/XYZ bounding-box subsets client-side. - Field Calculator upgrades: compute geometry length and area directly on your features. - Attribute table: multi-select rows with Ctrl and Shift, plus faster navigation. - Google Earth-style extras: "View in Google Maps / Google Earth" actions, camera-reset keyboard shortcuts, and a UTM easting/northing grid mode for the Gridlines overlay. - New plugins: a Mapillary coverage and street-level image viewer, a Historical Imagery panel, and an Elevation Profile tool. - Fully localized: all 13 language catalogs are complete, so the entire UI is translatable. Try it out - Launch GeoLibre Web: - GitHub: - Documentation: - Release notes: #GIS #GeospatialData #OpenSource #RemoteSensing #DataVisualization #MapLibre #GeoLibre

Qiusheng Wu

34,433 次观看 • 8 天前

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

AK

249,708 次观看 • 3 年前