12/24/2023 0 Comments Spacenet buildinglabels exampleFor the several Jupyter notebooks employed, I have extracted most of the code to external files that can be tested to keep the notebooks as easy to read and up-to-date as possible. I have written most of the code to be operable from the abfs command with tasks such as train, evaluate, and export. This value also extends to the top-level documentation, naming conventions, and accessibility through the command line interface. Therefore, my entire process has centered around reproducibility, maintainability, and interoperability.įor reproducibility, I have written automated unit tests for most of the core system including data extraction, train/val/test splits, training augmentation, and batch options. Although my masters focuses on the technical aspects of modeling and data engineering, I am also deeply interested in how to build machine learning (ML) systems end-to-end from data to production since I work for a software firm. Production-Oriented Approachīefore jumping into the project details, a simple elaboration of my approach will help clarify later discussion. Since they stored the information in a public AWS S3 storage bucket, the 3.4 GB of data was easily downloadable for local exploration of the links, formats, and imagery. In pursuit of an entire data science pipeline from raw annotated data to production-level API, I have focused on using only the tiles that include the visible spectrum in order to use a global tile-provider for the API.ĭue to the 8-week timeline of this entire project, I employed the +250,000 building annotations found in the SpaceNet Rio de Janeiro data set that covers over 2,500 square kilometers. Started last year, the SpaceNet challenge open sourced a set of manually labeled building footprints over five regions that includes visible spectrum (3-band), 8-band, and off-nadir tiles. in Data Science from Regis University, this project employs aerial imagery to automatically predict building footprints from a given latitude and longitude. Using Deep Learning and Computer Vision techniques, aerial imagery can be analyzed to automatically predict or augment structure annotations in both form and area. More specifically, determining roof area from a particular building footprint enables a rough estimate of potential work without a tedious outlining process of that building. In general, insights about house size and population density enable broad cross-section comparison of regions. Buildings are perhaps the most common landscape feature in aerial imagery.Īutomatic prediction of a building footprint can give rough estimates for both specific and general applications. From crop yield to war devastation, these images can tell a detailed story even from a single snapshot. The created polygons were compared to ground truth, and the quality of the solutions were measured using the SpaceNet metric.As satellite imagery has improved over the past ten years, applications of aerial images have dramatically increased. The main purpose of this challenge was to extract building footprints from increasingly off-nadir satellite images. Moving towards more accurate fully automated extraction of building footprints will help bring innovation to computer vision methodologies applied to high-resolution satellite imagery, and ultimately help create better maps where they are needed most. The ability to use higher off-nadir imagery will allow for more flexibility in acquiring and using satellite imagery after a disaster. In many disaster scenarios the first post-event imagery is from a more off-nadir image than is used in standard mapping use cases. Can you help us automate mapping from off-nadir imagery? In this challenge, competitors were tasked with finding automated methods for extracting map-ready building footprints from high-resolution satellite imagery from high off-nadir imagery.
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