![]() 8-bit RGB imagery support and 16-bit RGB imagery experimental support is available with Multi-Task Road Extractor Model (Multispectral imagery will be supported in the subsequent release). Both of these inputs, Imagery, and vector layer (for creating image chips and labels as 'classified tiles') are used to create data that is needed for model training.įigure 1: SpaceNet dataset - AOI 3 - Parisĭownloaded data has 4 types of imagery: Multispectral, Pan, Pan-sharpened Multispectral, Pan-sharpened RGB. The area of interest is Paris, with 425 km of 'road centerline' length (As shown in Figure. Vector labels as 'road centerlines' are available for download along with imagery, hosted on AWS S3. Classify roads, utilizing API's Multi-Task Road Extractor model.įor this sample, we will be using a subset of the publically available SpaceNet dataset.It will help in understanding the Multi-Task Road Extractor's workflow in detail. The models trained can be used with ArcGIS Pro or ArcGIS Enterprise and even support distributed processing for quick results.įurther details on the Multi-Task Road Extractor implementation in the API (working principle, architecture, best practices, etc.), can be found in the Guide, along with instructions on how to set up the Python environment.īefore proceeding through this notebook, it is advised to go through the API Reference for Multi-Task Road Extractor ( prepare_data(), MultiTaskRoadExtractor()). This sample shows how ArcGIS API for Python can be used to train a deep learning model (Multi-Task Road Extractor model) to extract the road network from satellite imagery. Road network is a required layer in a lot of mapping exercises, for example in Basemap preparation (critical for navigation), humanitarian aid, disaster management, transportation, and for a lot of other applications it is a critical component. □ Deep Learning and pixel-based classification. ![]() What is the impact on performance for a multiclass feature extraction challenge-i.e. How have algorithms that extract buildings and roads improved since SpaceNet was launched, and how can top algorithms from previous challenges be leveraged? SpaceNet 8 aims to answer these questions: Any winning open-source algorithm from SpaceNet 1-7 may also be used. During SpaceNet 8, challenge participants will train algorithms on imagery and labels from previous challenges-as well as newly created labeled training datasets from Maxar-to rapidly map an area affected by flooding. Since its launch in 2016, SpaceNet has made significant progress advancing open-source building footprint and road extraction algorithms. The goal of SpaceNet 8 is to leverage the existing repository of datasets and algorithms from SpaceNet Challenges 1-7 and apply them to a real-world disaster response scenario, expanding to multiclass feature extraction and characterization. To help address this need, the SpaceNet 8 Flood Detection Challenge will focus on infrastructure and flood mapping related to hurricanes and heavy rains that cause route obstructions and significant damage. As these events become more frequent and severe, there is an increasing need to rapidly develop maps and analyze the scale of destruction to better direct resources and first responders. Each year, natural disasters such as hurricanes, tornadoes, earthquakes and floods significantly damage infrastructure and result in loss of life, property and billions of dollars.
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