Keep in mind that it’s not meant for out-of-box use but rather for educational purposes. We will implement and train the network in PyTorch. We present easy-to-understand minimal code fragments which seek to create and train deep neural networks for the semantic segmentation task. Cross entropy loss with weight regularization is used during training. The architecture of a segmentation neural network with skip connections is presented below. Such an architecture makes it possible to learn fine-grained details which, when combined with a ResNet core encoder, significantly speeds up the training. Thanks to those connections, grain information about small details isn’t lost in the process. By propagating an image through several pooling layers, the resolution of feature maps is downsampled, which, due to information loss during pooling operations, results in low-resolution, coarse segmentation maps.Īs an improvement over a baseline fully-convolutional network, we used skip connections from higher resolution feature maps, recreating U-Net network architecture. This allows it to make predictions on arbitrary-sized inputs. It consists of only convolutional and pooling layers, without any fully connected layers. Network architecture overviewĪ baseline fully-convolutional network uses a simple encoder-decoder framework to solve semantic segmentation tasks. Your browser does not support the video tag. Unlike many other approaches, we use only RGB color information and no multispectral wavebands.

In this blog post we address the problem of satellite imagery semantic segmentation applied to building detection.

And that’s where machine learning-based solutions come into play.

Getting up-to-date information about roadblocks and threats is even more essential. Satellite mapping is way easier than traditional cartographic methods, but still, the main challenge is in recognizing particular objects in the image, like roads, buildings and landmarks. Such mapping was widely used during the recent humanitarian crises brought about by the earthquake in Haiti and the floods in Pakistan in 2010. Machine learning significantly reduces the time required to prepare an accurate map.Ĭrisis maps are often prepared by combining crowdsourced data, satellite and aerial imagery. Building maps to fit a crisis situation provides a challenge even when considering the impact of satellite imaging on modern cartography.

Imagery in a train to pakistan