We develop stand-alone prototypes, deliver production-ready software and provide mathematically sound consulting to inhouse data scientists. This post presents some key learnings from our work on identifying roofs on satellite images. SpaceNet Rio De Janeiro Points of Interest Dataset: SpaceNet’s dataset contains over 120,000 individual points that represent 460 of Rio de Janeiro’s features. This is a Keras based implementation of a deep UNet that performs satellite image segmentation. We chose parameters such that the obtained images had the best quality possible and most houses could be captured with a considerable margin in a single shot: The pixel size is variable along the latitude and its value can be calculated as follows: Here are some examples of the database images and their respective labels: The labels are the roof (blue), obstacles (magenta), ridges (green) and dormers (yellow). dida is your partner for AI-powered software development. Stanford Drone Dataset: This dataset from Stanford contains eight videos of various labeled agents moving through a variety of environments. Semantic segmentation of satellite images, $$\text{pixel size} = \frac{2 \pi \cdot \text{earth radius} \cdot \cos(\frac{\text{latitude} \cdot \pi}{180})}{256 \cdot 2^{\text{zoom level}}}.$$, $$\text{selu}(x) = \lambda \begin{cases} 1 Introduction In the field of computer vision, semantic segmentation in satellite images [8, 7] has been extensively employed to understand man-made features like roads, buildings, land use and land cover types. It is composed of an encoder followed by a decoder. Deep UNet for satellite image segmentation! $$, $$\ell_\text{dice}(\hat{Y},Y)= 1 - \frac{1}{K} \sum_{k=1}^K \frac{2 | \hat{Y}_k \odot Y_k |}{ | \hat{Y}_k |^2 + | Y_k |^2},$$, $$\hat{Y}_k,\: Y_k \in \{0,1\}^{d_1 \times d_2}$$, $${(\hat{Y}_k \odot Y_k)}_{(i,j)} = {\hat{Y}_k}_{(i,j)} {Y_k}_{(i,j)},$$, $$\tilde{w}_{ij} = w_0 \exp\left(-\frac{(c_1(i,j) + c_2(i,j))^2}{2\sigma^2} \right),$$, $$\ell_\text{wcce pw}(\hat{Y}, Y) = -\frac{1}{K}\sum_{i,j,k=1}^{d_1,d_2,K} (w_k+ \tilde{w}_{ij}) Y_{ijk}\log p_{ijk}$$, $$\ell_\text{dice pw}(\hat{Y},Y)= \ell_\text{dice}(\hat{Y},Y) + \ell_\text{wmse}(\hat{Y},Y),$$, $$\ell_\text{wmse}(\hat{Y},Y) = \frac{1}{K} \sum_{i,j,k}^{d_1,d_2,K} \tilde{w}_{ij}(\hat{Y}_{ijk} - Y_{ijk})^2.$$. Microsoft Canadian Building Footprints: Th… The Google Maps API was used to gather a total of 1500 unique images from houses spread across Germany. Open Cities AI Challenge: This high-resolution drone imagery dataset includes over 790,000 segmentations of building footprints from 10 cities across Africa. &p_{ijk} = \text{predicted probability that pixel } (i,j) \text{ belongs to class } k, \\ Plant Image Analysis: A collection of datasets spanning over 1 million images of plants. Clicking on an image leads youto a page showing all the segmentations of that image. It contains over 40,000 annotations of building footprints as well as a variety of landscape topology data. List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. They include everything from image datasets to named entity recognition datasets. weighted class categorical cross entropy: For an image with $$ d_1 \times d_2 $$ pixels and $$ K $$ classes the weighted class categorical cross entropy is defined as. This dataset contains 38 Landsat 8 scene images and their manually extracted pixel-level ground truths for cloud detection. MMSPG Mini-drone Video Dataset: Built to improve drone-based surveillance, this research dataset contains 38 HD videos. It depicts a range of different types of behavior and contains manual annotations of several different regions of interest. The following idealized pipeline illustrates the functionality of the planning tool: To achieve the proposed goal, we created a database with satellite images and the respective roof labels. Whether you need hundreds or millions of data points, our team of experts can ensure that your model has a solid ground truth. \end{align} Most of the datasets on this list are both public and free to use. This dataset contains satellite image and corresponding google map image of New York and divided into train and test set, which includes 1096 and 1098 image samples, respectively. very high resolution (VHR) satellite images. By Image-- This page contains the list of all the images. If you like what you see, be sure to check out our other dataset collections for machine learning. The results were analysed on three different land classification levels. The base-case accuracy reached in each case is respectively 74% and 83%. Powered by Microsoft Azure, Arccos’ virtual caddie app uses artificial intelligence to give golfers the performance edge of a real caddie. This dataset is regularly updated and sorted by year of survey. Okutama-Action: The 43 aerial sequences in the Okutama-Action dataset contain a wide range of challenges for those looking to develop human action detection algorithms. Because of that, we decided to follow the proposal of Olaf Ronneberger, et al. This dataset is frequently cited in research papers and is updated to reflect changing real-world conditions. For the full code go to Github. Satellite image. The dice loss is a continuous approximation of the well known dice coefficient. For all the geeks, nerds, and otaku out there, we at Lionbridge AI have compiled a list of 25 anime, manga, comics, and video game datasets. Methodology / Approach. Outside of Lionbridge, he loves to travel, take photos and listen to music that his neighbors really, really hate. Vertical Aerial Photography: More generally, the UK government has been collecting ortho-rectified aerial imagery since 2006. At Lionbridge AI, we share your obsession for building the perfect machine learning dataset. It’s intended for use in automating feature extraction. As previously featured on the Developer Blog, golf performance tracking startup Arccos joined forces with Commercial Software Engineering (CSE) developers in March in hopes of unveiling new improvements to their “virtual caddie” this summer. These are the plots of the mean dice coefficient obtained during training for the described loss functions: The wcce leads to better results on the training set but worse on the validation, which indicates that it does not generalize as well as the dice loss. With these two loss functions we were able to achieve satisfactory results, but we found out that by penalizing the most frequent class (background) we were failing to classify some very important pixels: the ones that belong to the background between very close roofs. This project can be very helpful to conduct experiments and further tests on semantic segmentation, either on satellite imagery or biomedical image datasets. In formulating our segmentation dataset we followed work done at Oak Ridge National Laboratory [Yuan 2016]. The images have 10 different classes, from roads to small vehicles. This project gets a score of 0.46 on the public test data set and 0.44 on the private test data set, which would rank the 7th out of 419 teams on the private leader board. The pixel weighting pw did not change the train plots very much, but on the validation set sped up the convergence of the dice loss. These agents include cyclists, pedestrians, and cars amongst others. Predicted Buildings. Our aim was to develop a planing tool for the placement of solar panels on roofs. The article introduces 10 open datasets for linear regression tasks and includes medical data, real estate data, and stock exchange data. About this project. This way we can enforce that some specific regions on the image are more important than others. List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. The National Geospatial-Intelligence Agency (NGA), a gov- ernment geospatial intelligence (GEOINT) organization, created a challenge [1] to advance more progress by providing a seg- mentation dataset for researchers and practitioners to segment circular objects in satellite … Learning and Adapting Robust Features for Satellite Image Segmentation on Heterogeneous Data Sets Abstract: This paper addresses the problem of training a deep neural network for satellite image segmentation so that it can be deployed over images whose statistics differ from those used for training. semantic segmentation of satellite images for land cover pre-diction. The code is on my github. Image Segmentation is a pixel level classification of an image. framework achieves a top F1 score of 81.2% on the gridded flood dataset and 83.5% on the gridded fire dataset. 0 & \text{if pixel } (i,j) \text{ does not belong to class } k. Newest datasets at the top of each category (Instance segmentation, object detection, semantic segmentation, scene classification, other). Train collection contains few tiff files for each of the 24 locations. Semantic Segmentation of Roads in Satellite imagery Pratyay Banerjee Kolkata, West Bengal 0 ... Semantic segmentation is the process of classifying each pixel of an image into distinct classes using deep learning. AIRS (Aerial Imagery for Roof Segmentation) is a public dataset that aims at benchmarking the algorithms of roof segmentation from very-high-resolution aerial imagery. This article will highlight some of the most widely-used coronavirus datasets covering data from all the countries with confirmed COVID-19 cases. Finally, the training hyper-parameters were obtained empirically using greedy optimization: All of these parameters played an important role in the training process, but the right choice of the loss function turned out to be crucial. as a self normalizing layer that extends and improves the commonly used ReLU activation: The authors claim that the main advantage of this activation is that it preserves the mean and variance of the previous layers. The following image illustrates the effect of skip layers on the segmentation results (FCN-32: no skip layers, FCN-16: one skip layer, FCN-8: two skip layers). Thanks to continued progress in the field of computer vision, there are several open-source drone datasets with aerial images on the Internet. The class weight was set so that the detection of the ridge, obstacles and dormers is enforced: In the same situation as above, the dice loss is defined as, are matrices containing the predictions for all pixels with respect to only class $$ k $$, respectively the ground truth and. Building segmentation on satellite images Sebastien Ohleyer´ ENS Paris-Saclay sebastien.ohleyer@ens-paris-saclay.fr Abstract Segmentation in remote sensing is a challenging task, especially concerning the classifier capacity to learn on a specific area of the earth and generalize to other regions. 1 & \text{if pixel } (i,j) \text{ belongs to class } k,\\ As an external consultant he is our go-to guy when it comes to pattern recognition in any kind of image data. Therefore, in this experiment, we generate google map image as a ground truth data from the given satellite image. In this post, I’ll be discussing image segmentation techniques for satellite data and using a pre-trained neural network from the SpaceNet 6 challenge to test an implementation out myself. Contact us now to discover how we can improve your data. For this purpose we set up a machine learning model that accurately partitions those images into different types of roof parts and background. Consequently, the second dataset collec-tion consists of 8-band images, which combines the first five bands of the original satellite images with the three bands of map images. BigEarthNet satellite image archive was selected for the research as one of two main datasets. Born and raised in the UK, he first came to Japan by chance in 2013 and is continually surprised that no one has thrown him out yet. 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