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1. #Spacenet buildinglabels example update
2. #Spacenet buildinglabels example manual
3. #Spacenet buildinglabels example series

2.2 Other Satellite Imagery CompetitionsĪ number of public challenges using satellite imagery have been run in the last couple of years. A recently released satellite imagery dataset is xVIEW xview_data, which contains 1400 km 2 of 30 cm satellite imagery labels, however, consist of bounding boxes that are not ideal for foundational mapping of buildings or roads. Another useful overhead dataset (though not so relevant to foundational mapping) is the COWC dataset cowc of cars, with 15cm aerial imagery collected over six different geographic regions labels consist of a point at the centroid of each car. The Massachusetts Roads Dataset MnihThesis contains 3-channel imagery at 1 meter resolution, and 2600 km 2 of coverage the imagery and labels are publicly available, though labels are scraped from OpenStreetMap and not independently collected or validated. The TorontoCity Dataset torontocity contains high resolution 5-10cm aerial 4-channel imagery, and ∼ 700 km 2 of coverage building and roads are labeled at high fidelity (among other items), but the data has yet to be publicly released. Imagery is obtained via an aerial platform and is 3 or 4 channel and 5-10cm in resolution. For example, the ISPRS semantic labeling benchmark isprs_semĭataset contains high quality 2D semantic labels over two cities in Germany and covers a compact area of 4.8 km 2 There is a demonstrated need for both automated building footprint extraction as well as automated road network extraction, as the Humanitarian OpenStreetMap Team’s Tasking Manager hotosmĬurrently has scores of open tasks for both roads and buildings.Įxisting publicly available labeled overhead or satellite imagery datasets tend to be relatively small, or labeled with lower fidelity than desired for foundational mapping. The potential application of automated building footprint and road network extraction ranges from the determination of optimal aid station locations during an epidemic in under mapped locations, to the establishment of an effective logistical schema in a disaster stricken region. The ability to create a road network is an important map feature (particularly if one is able to use this road network for routing purposes), while building footprint extraction serves as a useful proxy for population density zhangpop, popnature, popplos1. The third challenge addressed another foundational geospatial intelligence problem, road network extraction. The first two SpaceNet challenges focused on building footprint extraction from satellite imagery. In this paper we discuss the SpaceNet imagery, labels, evaluation metrics, prize challenge results to date, and future plans for the SpaceNet challenge series. The first two of these competitions focused on automated building footprint extraction, and the most recent challenge focused on road network extraction.

#Spacenet buildinglabels example series

The SpaceNet partners also launched a series of public prize competitions to encourage improvement of remote sensing machine learning algorithms. Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, and NVIDIA), released a large corpus of labeled satellite imagery on Amazon Web Services (AWS) called SpaceNet.

#Spacenet buildinglabels example update

We propose that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques.

#Spacenet buildinglabels example manual

Updating maps is currently a highly manual process requiring a large number of human labelers to either create features or rigorously validate automated outputs. Foundational mapping remains a challenge in many parts of the world, particularly in dynamic scenarios such as natural disasters when timely updates are critical.