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You can now track single or multiple objects in videos completely locally using Sam3 + MLX
mlx-vlm v0.4.2 is out ๐
New models
โข Sam3 by @facebook (+ realtime mask-only label drawing)
โข DOTS-MOCR by rednote-hilab
Fixes
โข Qwen3.5 RMSNorm dtype fix
โข LFM2-VL loads without torch
โข Magistral image token expansion fix
โข PaliGemma processor kwarg routing fix
โข Thinking defaults fixed in CLI + server
Shoutout to @pcuenq, and @mdstaff for his first contribution!
Get started today:
> uv pip install -U mlx-vlm
Leave us a star โจ
github.com/Blaizzy/mlx-vlm/rโฆ
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Visualizing the Strait of Hormuz shutdown using AIS tracking data.
Wild to see the precipitous drop off in transits from 100's of vessels per day to a handful.
The craziest part is zooming in to track a single Indian LPG tanker and watching it intentionally go dark -- turning off its AIS to sneak through the chokepoint, before popping right back on the radar on the other side.
OSINT is too much fun. Pulling this together to a bigger 4D god's eye view of the gulf region.
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Digital twin traffic visualization from CCTV
github.com/duy-phamduc68/Traโฆ
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You pick any location on earth and it is turned into a threejs environment within seconds
Holy fuck this could be insane!
Spent 20min on this, imagine if someone actually puts some effort into this
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We are thrilled to announce that Googleโs Satellite Embedding dataset, powered by @GoogleDeepMind's AlphaEarth Foundations model, has been updated for 2025. This additional year of coverage now unlocks the ability to look back, compare, and detect change across the planet with unprecedented clarity. Learn more here โก๏ธ goo.gle/3NdIxWn
Part of Google's Earth AI, the new data represents the state of the planet throughout 2025, distilling petabytes of multi-sensor data into a 64-dimensional embedding for every 10 meter pixel.
Whatโs new in this update? ๐งต๐
- ๐ 2025 Data: The state of the planet throughout 2025 is now available on the Earth Engine Data Catalog and Google Cloud Storage.
- ๐ฌ Unprecedented Change Detection: Because these embeddings capture subtle spectral, spatial and temporal signatures, they make it easy to spot significant year-over-year changes without the heavy lifting of raw image processing.
- ๐ Long-term Commitment: We are formalizing our commitment to the ongoing production of these annual layers to support your operational workflows.
Since we first launched the Satellite Embedding dataset, weโve been inspired by how our community is putting this data to work. Applications are ranging from ecosystem mapping and agricultural crop-typing to carbon stock prediction. We canโt wait to see what you do next.
#EarthEngine #GeoAI #DeepMind
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๐ข๐ป๐ฒ ๐บ๐ฒ๐บ๐ผ๐ฟ๐ ๐ฐ๐ฎ๐ปโ๐ ๐ฟ๐๐น๐ฒ ๐๐ต๐ฒ๐บ ๐ฎ๐น๐น.
We present ๐๐ผ๐๐ฒ๐ฅ, a new ๐ต๐๐ฏ๐ฟ๐ถ๐ฑ ๐บ๐ฒ๐บ๐ผ๐ฟ๐ architecture for long-context geometric reconstruction.
LoGeR enables stable reconstruction over up to ๐ญ๐ฌ๐ธ ๐ณ๐ฟ๐ฎ๐บ๐ฒ๐ / ๐ธ๐ถ๐น๐ผ๐บ๐ฒ๐๐ฒ๐ฟ ๐๐ฐ๐ฎ๐น๐ฒ, with ๐น๐ถ๐ป๐ฒ๐ฎ๐ฟ-๐๐ถ๐บ๐ฒ ๐๐ฐ๐ฎ๐น๐ถ๐ป๐ด in sequence length, ๐ณ๐๐น๐น๐ ๐ณ๐ฒ๐ฒ๐ฑ๐ณ๐ผ๐ฟ๐๐ฎ๐ฟ๐ฑ inference, and ๐ป๐ผ ๐ฝ๐ผ๐๐-๐ผ๐ฝ๐๐ถ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป.
Yet it matches or surpasses strong optimization-based pipelines. (1/5)
@GoogleDeepMind @Berkeley_AI
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Experimenting Gaussian Splatting with 360 images! Having omnidirectional views ensures no capture gaps, which makes spatial reconstruction so easy. Itโs honestly a blast to see the space come to life.
#GaussianSplatting #3DGS
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Esri just brought gaussian splatting to the web. ArcGIS Maps SDK 5.0 introduces a native GaussianSplatLayer for SceneView.
Splats now stream directly into browser-based GIS apps.
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Google DeepMind@GoogleDeepMindFeb 19
United Kingdom
United KingdomConnected via WebAccount-level information, not a live location or per-post device.
We used Gemini 3.1 Pro to build a realistic city planner app. ๐๏ธ
Watch how the model tackles complex terrain, maps out infrastructure, and simulates traffic to generate a high-quality visualization.
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Introducing anymap-ts: a new Python package that makes geospatial visualization easy!
Built on MapLibre + TypeScript, anymap-ts brings the same rich mapping experience to both the web and Jupyter Notebook. It supports many popular geospatial formats, including GeoJSON, GeoParquet, FlatGeobuf, PMTiles, COG, Zarr, COPC LiDAR, and Gaussian Splat.
It also includes powerful built-in UI components such as a minimap, bookmarks, print tool, view state manager, STAC search, street view, and geometry editor.
Try it out:
Web demo: opengeos.org/maplibre-gl-comโฆ
Jupyter demo: notebook.link/github/opengeoโฆ
GitHub: github.com/opengeos/anymap-tโฆ
Documentation: ts.anymap.dev
Video tutorial: youtu.be/Xr_UfBI46bc
#mapping #maplibre #typescript #jupyter #python
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Geospatial data science is all about having access to good data.
Here are a list of my favourite free geospatial resources:
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A critical gap in modern AI isn't language or vision. It's spatial grammar. And it reveals a fundamental data bottleneck.
We built MapTrace, a fully automated, generative AI pipeline (models act as creators/critics) to generate 2M high-quality map-path pairs. The result: Fine-tuning on this synthetic data lowered path-tracing errors and boosted the success rate by +6.4 points for Gemini 2.5 Flash on real-world maps.
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So these researchers figured out you can basically hallucinate 3D cities into existence using just satellite photos & a diffusion model.
The problem's pretty straightforward: satellites only see rooftops. Building facades? Invisible. Street-level detail? Doesn't exist. But people want flyable 3D environments, which means you need all that occluded geometry.
When I worked on google maps photogrammetry, we could only use satellite-based 3D for isolated stuff like the pyramids - anything city-scale required airplane flyovers. Which is fine until you hit aerial-denied regions where you literally can't fly. Huge chunks of the world just unavailable.
Their trick is honestly kind of beautiful. They train gaussian splats on satellite views, but as it descends toward ground level, the renders turn to absolute garbage - artifacts everywhere. Instead of fighting this, they just treat those nightmare renders as the input to a diffusion model. Basically - "hey FLUX, fix this mess."
Then here's where it gets clever: they generate multiple diffusion samples per view instead of committing to one. Because any single denoising path is probably wrong in 3D space, but if you generate a couple and let the GS optimization find consensus across them, you get actual geometric consistency.
They do this in episodes, curriculum style - start high, gradually descend (hence the name Skyfall-GS!). With each iteration the ground-level views get less fucked. By the end you've got real-time flyable cities that look surprisingly real, and the geometry still matches the satellite input.
No 3D training data. No street-level photos. Just satellites + diffusion doing what it does best - filling in the blanks. It's like neural scene completion but actually practical, and it unlocks basically the entire world.
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We are developing next generation defence technologies and innovation, with more than โฌ1 billion of funding!๐ข๐ช๐บ
With the adoption of the European Defence Fund Work Programme for 2025, we:
โก๏ธreduce the fragmentation of investment
โก๏ธstrengthen industrial competitiveness
โก๏ธpromote interoperability.
Learn more:
europa.eu/!Xtmmw3
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Behold the OSM category viewer downloader ... thingy
mapscaping.com/openstreetmapโฆ
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OpenStationMap openstationmap.org reveals indoor mapping data (where it has been added) specifically for railway stations
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Indoor AR navigation speed run!
It's taken innovations in mapping, indoor positioning and AR experience to arrive here. Iโve written a blog post to explain how it all works:
medium.com/dent-reality/dentโฆ
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Reliable worker communication and asset tracking for indoor and outdoor operations are key to boosting productivity and efficiency in complex industrial environments.
Find out how, together with our partners, we are helping enterprises transform their operations: nokia.ly/3OCBlk6
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The line-up for our first Geomob Berlin on eve of ๐๏ธ22 Feb is now set.
Talks by Alexis Batlle of @AVUXI, @IanHannigan of FORMATION, @StephanGeorg, Anna Kiss of @OrbicaGIS, and @igor_chubin followed by #geobeers ๐ป
Sign-up and details: thegeomob.com/post/feb-22nd-โฆ
#geomobBER































