Building Footprint Extraction - Africa

Name: Building Footprint Extraction - Africa
Creator: Esri
License: 暂无描述

Esri Aid & Development Team2026-03-28 收录

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<p style='margin-bottom:0.0001pt; background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial;'>This deep learning model is used to extract building footprints from high-resolution (10–40 cm) imagery. Building footprint layers are useful in preparing base maps and analysis workflows for urban planning and development, insurance, taxation, change detection, infrastructure planning, and a variety of other applications. <p style='margin-bottom:0.0001pt; background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial;'>Digitizing building footprints from imagery is a time-consuming task and is commonly done by digitizing features manually. Deep learning models have a high capacity to learn these complex workflow semantics and can produce superior results. Use this deep learning model to automate this process and reduce the time and effort required for acquiring building footprints. Using the model <div style='text-align:start;'>Follow the <a href='https://doc.arcgis.com/en/pretrained-models/latest/imagery/using-building-footprint-extraction-africa.htm' style='font-size:15px; font-weight:400;' target='_blank' rel='nofollow ugc noopener noreferrer'>guide</a> to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check <a href='https://github.com/esri/deep-learning-frameworks' style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:15px; font-weight:400;' target='_blank' rel='nofollow ugc noopener noreferrer'>Deep Learning Libraries Installer for ArcGIS</a>. Fine-tuning the model This model can be fine-tuned using the Train Deep Learning Model tool. Follow the <a href='https://doc.arcgis.com/en/pretrained-models/latest/imagery/finetuning-the-building-footprint-extraction-africa.htm' target='_blank' rel='nofollow ugc noopener noreferrer'>guide</a> to fine-tune this model. Input 8-bit, 3-band high-resolution (10–40 cm) imagery. Output Feature class containing building footprints. Applicable geographies The model is expected to work in Africa and gives the best results in Uganda and Tanzania. Model architecture The model uses the <a href='https://developers.arcgis.com/python/guide/how-maskrcnn-works/' style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:medium;' target='_blank' rel='nofollow ugc noopener noreferrer'>MaskRCNN</a> model architecture implemented using ArcGIS API for Python. Accuracy metrics The model has an average precision score of 0.786. Sample results Here are a few results from the model. To view more, see this <a href='https://arcg.is/08f4u5' target='_blank' rel='nofollow ugc noopener noreferrer'>story</a>.</div><p style='margin-bottom:0.0001pt; background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial;'> <p style='margin-top:0px; margin-bottom:0.0001pt; background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'><img src='https://ivt.maps.arcgis.com/sharing/rest/content/items/84174a61a5b74b54b799fa71ab55646b/data' /><p style='margin-top:0px; margin-bottom:0.0001pt; background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'> <p style='margin-top:0px; margin-bottom:0.0001pt; background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'><img src='https://ivt.maps.arcgis.com/sharing/rest/content/items/aa0e6b3124e44c9490d1802cd3624e9a/data' style='font-family:"Avenir Next", Avenir, "Helvetica Neue", Helvetica, Arial, sans-serif; font-size:15px;' /> <p style='margin-top:0px; margin-bottom:0.0001pt; background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'> <p style='margin-top:0px; margin-bottom:0.0001pt; background-image:initial; background-position:initial; background-size:initial; background-repeat:initial; background-attachment:initial; background-origin:initial; background-clip:initial; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; font-size:16px;'><img src='https://ivt.maps.arcgis.com/sharing/rest/content/items/4fd954303a544df7b39e4111cfc0cf72/data' />

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