The techniques here can be applied in many different situations and we hope this concrete example serves as a guide to tackling your specific problem. With the sample project that accompanies this blog post, we walk you through how to train such a model on an Azure Deep Learning Virtual Machine (DLVM). Three deep learning models are now available in ArcGIS Online. For those eager to get started, you can head over to our repo on GitHub to read about the dataset, storage options and instructions on running the code or modifying it for your own dataset. (Watch for more models in the future!). A final step is to produce the polygons by assigning all pixels predicted to be building boundary as background to isolate blobs of building pixels. After epoch 10, smaller, noisy clusters of building pixels begin to disappear as the shape of buildings becomes more defined. 3. We also created a tutorial on how to use the Geo-DSVM for training deep learning models and integrating them with ArcGIS Pro to help you get started. Such tools will finally enable us to accurately monitor and measure the impact of our solutions to problems such as deforestation and human-wildlife conflict, helping us to invest in the most effective conservation efforts. The count of true positive detections in orange is based on the area of the ground truth polygon to which the proposed polygon was matched. For those eager to get started, you can head over to our repo on GitHub to read about the dataset, storage options and instructions on running the code or modifying it for your own dataset. We observe that initially the network learns to identify edges of building blocks and buildings with red roofs (different from the color of roads), followed by buildings of all roof colors after epoch 5. We also created a tutorial on how to use the Geo-DSVM for training deep learning models and integrating them with ArcGIS Pro to help you get started. Each plot in the figure is a histogram of building polygons in the validation set by area, from 300 square pixels to 6000. Such tools will finally enable us to accurately monitor and measure the impact of our solutions to problems such as deforestation and human-wildlife conflict, helping us to invest in the most effective conservation efforts. As high-resolution satellite images become readily available on a weekly or daily basis, it becomes essential to engage AI in this effort so that we can take advantage of the data to make more informed decisions. In June 2018, our colleagues at Bing announced the release of 124 million building footprints in the United States in support of the Open Street Map project, an open data initiative that powers many location based services and applications. As part of the AI for Earth team, I work with our partners and other researchers inside Microsoft to develop new ways to use machine learning and other AI approaches to solve global environmental challenges. This repository contains a walkthrough demonstrating how to perform semantic segmentation using convolutional neural networks (CNNs) on satellite images to extract the footprints of buildings. The model was trained on large quantities of U.S. imagery datasets (30-60 cm resolution). The semantic segmentation model (a U-Net implemented in PyTorch, different from what the Bing team used) we are training can be used for other tasks in analyzing satellite, aerial or drone imagery – you can use the same method to extract roads from satellite imagery, infer land use and monitor sustainable farming practices, as well as for applications in a wide range of domains such as locating lungs in CT scans for lung disease prediction and evaluating a street scene. Navigate to Analysis > Tools 4. Another parameter unrelated to the CNN part of the procedure is the minimum polygon area threshold below which blobs of building pixels are discarded. The workflow consists of three major steps: (1) extract training data, (2) train a deep learning feature classifier model, (3) make inference using the model. Visualise few samples from your Training data. A powerful, low-code platform for building apps quickly, Get the SDKs and command-line tools you need, Continuously build, test, release, and monitor your mobile and desktop apps. This image features buildings with roofs of different colors, roads, pavements, trees and yards. We can see that towards the left of the histogram where small buildings are represented, the bars for true positive proposals in orange are much taller in the bottom plot. The geospatial data and machine learning communities have joined effort on this front, publishing several datasets such as Functional Map of the World (fMoW) and the xView Dataset for people to create computer vision solutions on overhead imagery. “We wanted to use machine learning to extract street data and building footprints from the satellite imagery while using the minimum amount of human input.” Deep Learning to the Rescue Deep learning, a powerful form of AI, involves teaching a computer to detect patterns in large amounts of data, and to recognize and extract just the information you want. These are transformed to 2D labels of the same dimension as the input images, where each pixel is labeled as one of background, boundary of building or interior of building. Original images are cropped into nine smaller chips with some overlap using utility functions provided by SpaceNet (details in our repo). The techniques here can be applied in many different situations and we hope this concrete example serves as a guide to tackling your specific problem. Each plot in the figure is a histogram of building polygons in the validation set by area, from 300 square pixels to 6000. We show how to carry out the procedure on an Azure Deep Learning Virtual Machine (DLVM), which are GPU-enabled and have all major frameworks pre-installed so you can start model … Load an Intermediate model to train it further. The optimum threshold is about 200 squared pixels. We used Classify pixels using deep learning tool to segment the imagery using the model and post-processed the resulting raster in ArcGIS Pro to extract building footprints… The labels are released as polygon shapes defined using well-known text (WKT), a markup language for representing vector geometry objects on maps. We will discuss more with the suitable freelancer. Illustration from slides by Tingwu Wang, University of Toronto (source). An example of infusing geospatial data and AI into applications that we use every day is using satellite images to add street map annotations of buildings. It was found that giving more weights to interior of building helps the model detect significantly more small buildings (result see figure below). After epoch 7, the network has learnt that building pixels are enclosed by border pixels, separating them from road pixels. We use a Fully Convolutional Neural Network to extract bounding polygons for building footprints. Our network takes in 11-band satellite image data and produces signed distance labels, denoting which pixels are inside and out- side of building footprints. In this post, we highlight a sample project of using Azure infrastructure for training a deep learning model to gain insight from geospatial data. Since this is a reasonably small percentage of the data, we did not exclude or resample images. 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