Posts

Nano Face: A Mac Camera, a Jetson, and One USB Cable

Image
Let me get the obvious out of the way first. Face detection on a Jetson is not new. NVIDIA ships samples for it, half the tutorials on the internet do it, and I am fairly sure somebody has done it on a Raspberry Pi wearing a HAT. So no, this is not a post about discovering face detection. This is a post about my two Jetsons, an Orin Nano Super and an AGX Orin, that live headless on a shelf with no monitor, no keyboard, and no camera plugged into either of them. I had just finished moving the Nano to JetPack 7.2.1 and pruning both boards down to a lean server install (no desktop, no Docker, nothing starting at boot), and I wanted a small, honest, end-to-end test that they were alive and doing real work. A face detector is a good smoke test. It either draws a box on your nose or it does not. The twist, and the part I actually cared about, is where the camera lives. It is the one in my MacBook Pro. The Jetson never sees a camera device. Frames leave the Mac...
Post a Comment
Read more

ArcGIS Pro, Claude Code, and a Loopback Bridge

Continuing the GenAI-with-a-GeoSpatial-twist thread I started a while back. Back then, I had a language model reason about geospatial logic. This time, I wanted it to actually do the work—on my open project, inside my running ArcGIS Pro session, while I watch. The result is ProCowork , an experimental native ArcGIS Pro add-in that embeds the Claude Code engine in a dockable chat panel. I can ask it to list layers, add and calculate fields, select features, run a buffer, or write a more specialized ArcPy script. Claude generates the code, the add-in runs it against the live project, and the code and results come back into the same panel. For example, I can type: “List the layers in the current map.” “Add a DOUBLE field POP_DEN to Parcels and set it to POP / AREASQMI.” “Select parcels where POP_DEN > 5000 and zoom to them.” “Buffer Roads by 100 meters and add the result to the map.” The panel is useful, but the interesting part is ...
Post a Comment
Read more

One Lander, Three Controllers: Reinforcement Learning, MPC, and Quadratic Programming

Image
I was always interested in the Lunar Lander game and suddenly I heard about model predictive control and quadratic programming. I want to see if the Lunar Lander RL can be adapted to that one in here. That question led me to build three related experiments around Gymnasium's LunarLander-v3 : Lunar RL : a causal-transformer PPO policy trained through reinforcement learning. Lunar MPC : adaptive model predictive control using discrete beam search. Lunar QP : model predictive control whose plan is solved as a convex quadratic program. All three control the same lander, but they answer very different questions. RL asks, “Can experience produce a policy that maps observations directly to actions?” MPC asks, “Given an approximate physics model, which short action sequence looks best from the current state?” QP asks, “Can that short-horizon plan be expressed as a fast convex optimization problem with explicit constraints?” The progression is not a conv...
Post a Comment
Read more

Teaching a Lunar Lander to Stop Hovering: DQN, Prioritized Replay, and n-Step Returns

Image
LunarLander looks simple: observe eight numbers, choose one of four engine actions, and guide a small craft between two flags. In practice, it exposes several failure modes that make Deep Q-Learning interesting. My first promising agents learned to approach the landing pad, then hovered above it until the episode timed out. Other runs climbed past a score of 100 and later collapsed. I built LunarLanderDRL to work through those problems in a compact PyTorch implementation. The final agent combines a Deep Q-Network (DQN), proportional prioritized experience replay, Double DQN, Huber loss, Polyak target updates, and three-step returns. Across three training seeds, the resulting checkpoints averaged 265.7 on 90 held-out episodes and recorded zero crashes . Two consecutive rollouts of the shipped policy, scoring 308.1 and 272.1. The problem Gymnasium's LunarLander-v3 returns an eight-dimensional observation: horizontal and vertical position, ...
Post a Comment
Read more

On Using AutoML for NYC Taxi Trip Duration Prediction

While explaining   Optuna   to a client in the context of hyperparameter tuning, and performing more research on the topic, I came across   AutoGluon   to perform " AutoML   for images, text, and tabular data". After a quick scan of the documentation, I decided to give it a try and see how it performs on a simple project. I always loved the Kaggle competition NYC Taxi Trip Duration, as the data has spatial, temporal, and other traditional attribute information and is a great dataset to test various models and feature engineering techniques. I used a local  Apache Spark  instance (as it is my go-to ETL engine) to perform some feature engineering before letting AutoGluon do its magic. As a quick proof of concept, the results are quite impressive, and here are the steps to reproduce the project and visualize the result.
Post a Comment
Read more

Arabic SDK For Apache Spark

I recently attended the Esri Saudi Arabia User Conference and was amazed by the changes in the Kingdom. The capital city of Riyadh is booming and proliferating. During the conference, I presented on integrating GenerativeAI and GIS in the plenary session and led a session on BigData and GeoAnalytics Engine . GeoAnalytics Engine, based on Apache Spark , allows spatial operations on Spark data frames. We showcased a project called "A Day in the Life," which used historical traffic data from HERE to demonstrate traffic congestion during peak hours. Traffic is notoriously bad in the city, so this was a fitting example. My colleague Mahmoud H. presented a traditional workflow process in a Jupyter Notebook off a Google Cloud DataProc cluster, efficiently processing over 300 million records (this is relatively "small"). The processed traffic information was then displayed in ArcGIS Pro in a time-aware layer to reflect the congestion visually while activating a time sli...
Post a Comment
Read more

Back in Action: GenAI Meets GeoSpatial

 Hello, everyone. It has been a while since my last post, and I wanted to explain my absence. I have been working on demanding client projects requiring confidentiality, so I couldn't share anything. But now I'm back and excited to dive into something new and exciting.  Generative AI (GenAI) has gained much attention lately, but I'm taking it to a different level by merging Large Language Models (LLMs) with insights from geospatial analysis. It's GenAI with a GeoSpatial twist. I want to introduce a simple project, " ReAct geospatial logic with Ollama ," which uses resources such as the Python Langchain and Ollama . I'm thrilled to be back and can't wait to start this new journey with you. Keep an eye on this space for future updates, tips, and unique code snippets. Your feedback and questions are valuable, so please don't hesitate to reach out. I'll see you in the next post, and as usual, you can check out the source code here .
Post a Comment
Read more