Using CosmiQ’s Solaris library to fine-tune models pre-trained on SpaceNet overhead imagery — Summary: Using Solaris, you can fine-tune deep learning models pre-trained on overhead imagery for five minutes and achieve performance comparable to past SpaceNet Challenge prize-winners. The 5th round of the popular SpaceNet Challenge series has begun! There, participants are challenged to develop models that can extract routable road networks with travel time predictions from overhead imagery. Past iterations of the challenge have been dominated by deep learning models painstakingly trained for days, often using multiple…

Performing machine learning (ML) and analyzing geospatial data are both hard problems requiring a lot of domain expertise. These limitations have historically meant that one needs to be an expert in both to perform even the most basic analyses, making advances in AI for overhead imagery difficult to achieve. …

Parsing out the similarities, differences, and limitations for different geospatial image segmentation models — SpaceNet’s mission is to accelerate geospatial machine learning and is supported by the SpaceNet member organizations. To learn more visit https://spacenet.ai. This is the final post in our series about the SpaceNet Challenge Round 4: Off-Nadir Building Detection Challenge. For an intro to the dataset, see this post. …

Augmentation approaches, loss functions, and learning objectives — Note: SpaceNet’s mission is to accelerate geospatial machine learning and is supported by the SpaceNet member organizations. To learn more visit https://spacenet.ai. The SpaceNet Challenge Round 4: Off-Nadir Building Detection Challenge hosted by TopCoder completed recently, and we’ve had an opportunity to examine the competitors’ solutions. In this post we…

SpaceNet’s mission is to accelerate geospatial machine learning and is supported by the SpaceNet LLC member organizations. The SpaceNet Challenge: Round 4, Off-Nadir Building Footprint Extraction hosted by TopCoder is complete! Congratulations to cannab, selim_sef, MaksimovKA, number13, and XD_XD for taking home the top 5 prizes. Check out this GitHub…

Note: SpaceNet’s mission is to accelerate geospatial machine learning and is supported by the SpaceNet member organizations. To learn more visit https://spacenetchallenge.github.io/. This post is part 3 of a series about the SpaceNet 4: Off-Nadir Dataset and Building Detection Challenge. …

Note: SpaceNet is a collaborative effort between CosmiQ Works, Digital Globe and Radiant Solutions hosted on Amazon Web Services as a public dataset. To learn more visit https://spacenetchallenge.github.io/. This post is part 2 of a series about the SpaceNet 4: Off-Nadir Dataset and Building Detection Challenge. …