{"action":"created","comment":{"url":"https://api.github.com/repos/scipy-conference/scipy_proceedings/pulls/comments/195939627","pull_request_review_id":129399141,"id":195939627,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDE5NTkzOTYyNw==","diff_hunk":"@@ -0,0 +1,387 @@\n+:author: Virginia Ng\n+:email: virginia@mapbox.com\n+:institution: Mapbox\n+\n+\n+:author: Daniel Hofmann\n+:email: hofmann@mapbox.com\n+:institution: Mapbox\n+\n+\n+--------------------------------------------------------------\n+Scalable Feature Extraction with Aerial and Satellite Imagery\n+--------------------------------------------------------------\n+\n+.. class:: abstract\n+\n+   In this talk we introduce use cases for feature extraction with aerial\n+   and satellite imagery such as turn lane marking detection, road and\n+   building footprint detection. In doing so, we show the potential of\n+   large-scale object detection and segmentation on aerial and satellite\n+   imagery. \n+\n+   We give insights into how imagery training sets can be built\n+   with little or no human annotations needed making use of available\n+   datasets such as OpenStreetMap. We then focus on modeling aspects for\n+   object detection and segmentation. In doing so we give an insight into\n+   state-of-the-art detection systems and adaptations we had to make for\n+   the aerial and satellite imagery domain. \n+\n+   We conclude the talk with lessons learned in building these \n+   large-scale object detection and segmentation pipelines, and show \n+   potential for future work in this\n+   domain.\n+\n+.. class:: keywords\n+\n+   computer vision, deep learning, neural networks, satellite imagery, aerial imagery\n+\n+\n+I. Mapbox\n+---------\n+\n+Mapbox is the location data platform for mobile and web applications. We\n+provide building blocks to add location features like maps, search, and\n+navigation into any experience you create.\n+\n+Location is built into the fabric of our daily experiences. Whether\n+you’re exploring a city with Lonely Planet, sharing with friends on\n+Snapchat, seeing if it’s going to rain on Weather.com, tracking breaking\n+news on Bloomberg — location is essential to every one of these\n+applications, and they’re powered by Mapbox.\n+\n+II. Navigation\n+--------------\n+\n+In particular, our navigation products are focused on providing smart\n+turn-by-turn routing based on real-time traffic. Valuable Assets For\n+Routing, Maps, and Geocoding inlcude\n+\n+turn Restrictions, turn lane markings, parking lots, buildings, grass,\n+trees, parks, water, bridges.\n+\n+We can manually map them, or we can abstract these assets from imagery.\n+\n+III. Designed with Open-Source Tools\n+-------------------------------------\n+\n+We designed our processing pipelines and tools with open-source\n+libraries like Scipy, Rasterio, Fiona, Osium, JSOM, Keras, PyTorch,\n+OpenCV etc, while our training data was compiled from\n+OpenStreetMap [osm]_ and Mapbox Maps\n+API [mapbox]_.\n+\n+IV. Scalable Feature Extraction Pipelines\n+-----------------------------------------\n+\n+In this talk we introduce step-by-step how we scale object detection and\n+semantic segmentation pipelines (Figure 1). Two examples we will\n+present are turn lane markings and parking lot segmentation with aerial\n+and satellite imagery.\n+\n+.. figure:: fig1.png\n+   :height: 100 px\n+   :width:  200 px\n+   :scale: 39 %\n+\n+   Feature Extraction Pipeline. \n+\n+\n+1. Data\n+--------\n+\n+**Data Preparation.** Before we talk about data, we would first like to\n+explain the difference between object detection and semantic\n+segmentation. These are two different problem spaces in computer vision.\n+Object detection is the problem of locating and classifying a variable\n+number of objects in an image. Here we use object detection models to\n+detect turn lane markings from satellite imagery (Figure 2). Other\n+practical applications of object detection include face detection,\n+counting, visual search engine.\n+\n+.. figure:: fig2.png\n+   :height: 75 px\n+   :width:  150 px\n+   :scale: 22 %\n+\n+   Turn lane markings detection.\n+\n+Semantic segmentation on the other hand, not only locates and classifies\n+objects, it does so at pixel level. For example, in addition to\n+recognizing the road from the buildings, we also have to delineate the\n+boundaries of each object (See Figure 3)\n+\n+.. figure:: fig3.png\n+   :height: 100 px\n+   :width: 200 px\n+   :scale: 22 %\n+\n+   Semantic segmentation on roads, buildings and greens\n+\n+To prepare training data for detecting turn lane markings, we first find\n+where the turn lane markings are. OpenStreetMap is a collaborative\n+project to create a free editable map of the world. Turn lane markings\n+on OpenStreetMap are recorded as “ways” (line-strings) [osm-lanes]_. We used a tool\n+called Overpass Turbo [overpass]_ to query\n+OpenStreetMap turn lane markings. We then extracted GeoJSONs in 5 cities\n+from OpenStreetMap that have one of the following attributes\n+(“\\turn:lane=*”, “\\turn:lane:forward=*”, “\\turn:lane:backward=*” ) and\n+created a custom layer over the `mapbox.satellite\n+layer` [mapbox]_.\n+We annotated (draw bounding box around the turn lane markings) six\n+classes of turn lane markings: “\\Left”, “\\Right”, “\\Through” (straight),\n+“\\ThroughLeft”, “\\ThroughRight”, “\\Other” using JOSM [josm]_ in 5 cities, over 53K turn\n+lane markings. JOSM is an extensible editor for OpenStreetMap (OSM) for\n+Java 8+. It supports loading GPX tracks, background imagery and OpenStreetMap data\n+from local sources as well as from online sources and allows to edit the\n+OpenStreetMap data (nodes, ways, and relations) and their metadata tags. It 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