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Products that do what Feature detection exploration in Lidar DEMs via differential decomp does
I'm not a geospatial expert — I work in AI/ML. This started when I was exploring LiDAR data with agentic assitince and noticed that different signal decomposition methods revealed different terrain features. The core idea: if you systematically combine decomposition methods (Gaussian, bilateral, wavelet, morphological, etc.) with different upsampling techniques, each combination has characteristic "failure modes" that selectively preserve or eliminate certain features. The differences between outputs become feature-specific filters. The framework tests 25 decomposition × 19 upsampling…
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2018 · scaleapi.com
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This is our Atlas of Water Science. It's a globe mapping water science that colleagues have done over the past few years. Approx 300 papers got analyzed. The pipeline sends open access papers to an LLM asks the LLM to extract areas relevant to science in a form suitable for a geocoder, then we use a geocoder to get lat lons. When things happen in the same spot we jitter the locations a bit to differentiate things. gpt4o-mini was used for most of the analysis. Some of the features on the website are directly invented by Claude. There are some errors when geocoding went wrong or locations were…
2024 · wateratlas.webapp.csiro.au
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2017 · mjmdavis.com
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I spent years manually editing large LiDAR point clouds—and I hated every moment of doing this. To make things easier, my team and I did extensive research and development on the latest state-of-the-art techniques for point cloud processing. We built a massive training dataset and trained semantic segmentation networks, all packaged into an AI-powered platform called Flai. With Flai, you can upload, view, and classify your point clouds into over 30 categories, including buildings, power lines, vehicles and many more. It’s free to try ( https://app.flai.ai/register ), and I’d…
2024 · flai.ai
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2021 · github.com
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The most common failures for production agents are behavioral: looping, reasoning leakage, user frustration, and more. Using a frontier model like GPT or Sonnet to judge every turn is too expensive and slow to run at scale. To solve this, we built Reflexes: semantic signals from agent traces, served fast and cheap over API. Built on custom kernels and a custom inference engine forked from vLLM. Under the hood, it is a small LLM architected around multi-head inference. Small models need to be trained for specific tasks, but running 50 separate small models on the same input for 50 tasks makes…
Jun 2026
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We built Maptoolkit.org - a free, production-grade vector maps service built on OpenStreetMap and MapLibre. Map rendering and tile hosting have historically been either expensive (Google, Mapbox) or complex to maintain yourself. (Plus: Their maps are car-focused.) Our maps feature max data zoom 15, global hill-shading, 3D terrain, contour lines, water depths, and specialized styles (like Hiking, Cycling, Winter). No sign up or API keys required. You can use it like this: ``` const map = new maplibregl.Map({ container: 'map', style: 'https://styles.maptoolkit.org/summer.json',…
Jul 2026 · maptoolkit.org
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Hello HN, I recently posted a work-in-progress paper, along with code necessary for replicating all its results, at: https://github.com/glassroom/heinsen_routing Among other things, the code in this repo outperforms Hinton et al.'s recent state-of-the-art result in visual recognition[0] while requiring fewer parameters and an order-of-magnitude fewer training epochs. Most of the original research we do at work tends to be either proprietary in nature or tightly coupled to internal code, so we cannot share it with the world. In this case, however, I was able to remove all…
2019
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When new Large Multimodal Models (LMMs) are released, there is excitement as we explore new capabilities. What can a model do? What can't a model do? What strange behaviors does the model exhibit? With that said, such analyses are frozen in time. At a hackathon toward the end of last year, the Roboflow team made a tool that runs the same set of tests with the GPT-4 with Vision API every day. This allows people to see how the model performs over time as updates are made. The last seven days of results are displayed on a web page; the rest of the data is archived in GitHub. We started the site…
2024 · gptcheckup.com
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Hey HN, when building ML systems for industrial AI, we have learned that data inspection is critical during the ML development process. We are also big fans of the Hugging Face ecosystem. That is why we built an integration to our data exploration tool Spotlight that allows you to interactively explore Hugging Face datasets with one line of code. Spotlight lets you leverage model results such as predictions and embeddings to gain a deeper understanding in data segments and model failure modes. Currently, many many NLP, CV, Audio and multimodal datasets are supported both locally and on the…
2023 · huggingface.co
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Submarius started as just a dashboard for diving conditions, but I quickly realized that there is simply no API for water clarity, nor even a simple way to calculate it. Divers call it viz, and it is one of the most consequential factors for a diving experience. So I dove the viz rabbit-hole. I landed on a state-augmented ensemble kalman filter. More details on the /clarity page. My question for the HN audience is if anyone knows of a better way of doing this? There is a ton of satellite data to train on, but a lot of it is messy (clouds read as turbid water, shallow reefs reflect…
Jun 2026 · submarius.com
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Hi HN, I'm Kaushik, and I built Rocketgraph. I believe that while other spaces have caught up to the AI wave, the observability space is still lagging behind, using the same tools and dashboards that we use to analyse logs from human-written code. But now the code is written and debugged by AI, so we need to rethink how we do observability where the observer itself is an AI. The problem that I run into is when an alert fires, I have to manually check the Grafana dashboards and write LogQL queries, which is pretty much like greping. But production usually breaks due to a schema mismatch, or a…
Jun 2026 · github.com
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2023 · github.com
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I’ve built vectorvector, an open-source CLI tool for working with elevation data. Key features: * Generates contour lines (vector), hillshade tiles (raster), and terrarium tiles (raster elevation) * Processes the entire planet in ~7 hours on consumer hardware * Algorithms: Marching Squares for contours, Horn for hillshade, and Ramer–Douglas–Peucker for line simplification Benchmarked on a MacBook Pro (M2, 64 GB RAM). Output is customizable and aimed at developers and GIS enthusiasts. Would love to hear your thoughts and feedback! Repo:…
2024 · github.com
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Starting today, DirectAI’s Zero-Shot Image Classification & Object Detection APIs are public. Define classes and objects exclusively in natural language - no training data required. And if something goes wrong, you can resolve the edge case in natural language too! We’ve been hard at work to bring powerful and controllable computer vision to everybody. To do this, we’re building novel ways of interacting with the knowledge stored in large foundation models. We’re bootstrapping from zero-shot methods to create new approaches that allow for more control over decision boundaries, without…
2023 · github.com
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2020 · terraformdriftdetection.com
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Hey everyone, I wanted to share what I have been working on recently. I had this itch for a long time to figure out if terrain contour matching could help with ground navigation, by only only relying on iphone's sensors and public data. So far I have had decent results. This is a working prototype, but there is a lot that is in the pipeline. If you notice anything weird, let me know or open a PR. Any feedback, comments are welcome :)
Jun 2026 · github.com
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