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connorcrowe/README.md

🌎 Data, Product, Geospatial

Product Manager who likes getting hands-on with geospatial data.

Featured Projects

🚧 Current: Geoff, the Spatial-AI Map-Maker

GEOFF (GEOspatial Fact Finder) aims to turn natural language prompts like "how many bike lanes are near school zones?", turn them into Spatial SQL, and display the results on a web map. Project progressing ~ Live Demo | 🔗 GitHub Repo

🗺️ High-Resolution Automated LULC Classification at Scale in Toronto

Using trained LULC U-net classifier to autoamtically predict land use land cover of the entire City of Toronto aerial at 2 px per meter. Technologies: gdal, leaflet, Vite, Tensorflow/Keras, Python

🗺️ Live Demo | 🔗 GitHub Repo

🛩️ Land Use Land Cover Neural Network Classification from Aerial Data

Trained a Convolutional Neural Network (CNN) with U-net architecture on aerial imagery to classify land use in downtown Toronto. Technologies: CNN, U-net, Tensorflow/Keras, Map Digitization

🔗 GitHub Repo

🌆 Urban Heat Island & Vulnerability Analysis of Toronto from Satellite Imagery

Mapped urban heat islands in Toronto using remote imagery and overlaid demographic data to highlight vulnerable communities. Technologies: QGIS, GDAL, Raster Analysis, Remote Sensing, Landsat

🗺️ Full Story on StoryMaps! | 🔗 GitHub Repo

🚲 Geospatial Analysis of Toronto Bike Share Data

Analyzed Toronto's bike share data (2016-2024) using spatial SQL and geospatial visualization to assess impact of changes in infrastructure. Technologies: PostGIS, QGIS, Python, PyQGIS

🗺️ Full Story on StoryMaps! | 🔗 GitHub Repo

Pinned Loading

  1. geoffgeoffPublic

    Geoff (GEOspatial Fact Finder) is a geospatial AI that turns natural language into spatial queries and displays results on a web map

    Python 6

  2. to-lulc-scaleto-lulc-scalePublic

    This project uses a U-Net CNN to classify land use for the entire City ot Toronto at high-resolution in an automated pipeline.

    Jupyter Notebook 9

  3. to-lulc-aimlto-lulc-aimlPublic

    Land use land cover (lulc) classification of aerial imagery using machine learning techniques including U-Net architecture Convolutional Neural Networks (CNNs).

    Jupyter Notebook 2 1

  4. to-urban-heat-islandto-urban-heat-islandPublic

    Geospatial analysis of remote imagery to identify where the urban heat island effect is worst in Toronto, Canada, and which areas have the population most vulnerable to them.

    1

  5. to-bike-analysisto-bike-analysisPublic

    Statistical and geospatial analysis of Toronto Bike Share data and what it can tell us about the impact of changes to Toronto's bicycle infrastructure

    Jupyter Notebook 1

  6. dcp-sortdcp-sortPublic

    A sorting algorithm designed for improved time complexity on a massively parallel system

    JavaScript 1