satellite image segmentation dataset

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. 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. DOTA: A Large-scale Dataset for Object Detection in Aerial Images: The 2800+ images in this collection are annotated using 15 object categories. &p,\: Y \in \{0,1\}^{{d_1}\times {d_2}\times K}, \\ It depicts a range of different types of behavior and contains manual annotations of several different regions of interest. &Y_{ijk} = \begin{cases} The Zurich Urban Micro Aerial Vehicle Dataset: This dataset includes video of around 2km of urban streets at a low altitude. 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. The results were analysed on three different land classification levels. That’s why we’ve compiled this collection of datasets to get your project off to a good start. Methodology / Approach. 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. Stanford Drone Dataset: This dataset from Stanford contains eight videos of various labeled agents moving through a variety of environments. Our array of data creation, annotation, and cleaning services are built to suit your specialist requirements. It was designed for pixel-wise labeling use cases and includes a diverse range of terrain, from densely populated cities to small towns. semantic segmentation of satellite images for land cover pre-diction. Our aim was to develop a planing tool for the placement of solar panels on roofs. Original Medium post; Theory. Lionbridge brings you interviews with industry experts, dataset collections and more. Next we present some of the obtained results. 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. BigEarthNet satellite image archive was selected for the research as one of two main datasets. Dataset. A huge dataset for binary segmentation of clouds in satellite images - SorourMo/95-Cloud-An-Extension-to-38-Cloud-Dataset If he works with aerial or satellite images, which are usually very large, it is even worse. These agents include cyclists, pedestrians, and cars amongst others. This is a Keras based implementation of a deep UNet that performs satellite image segmentation. The API accepts as input a value of latitude and longitude that identifies a specific region on the globe and a set of parameters to select the desired characteristics of the returned image. Newest datasets at the top of each category (Instance segmentation, object detection, semantic segmentation, scene classification, other). 20 Free Sports Datasets for Machine Learning, 12 Product Image Databases and Supermarket Datasets, DOTA: A Large-scale Dataset for Object Detection in Aerial Images, SpaceNet Rio De Janeiro Points of Interest Dataset, Aerial Imagery Object Identification Dataset, The Zurich Urban Micro Aerial Vehicle Dataset, Top 25 Anime, Manga, and Video Game Datasets for Machine Learning, Top 10 Vietnamese Text and Language Datasets, 12 Best Turkish Language Datasets for Machine Learning, 16 Strange, Funny, and Weird Datasets for Machine Learning, 14 Free Agriculture Datasets for Machine Learning, 14 Best Movie Datasets for Machine Learning Projects, 10 Free Marketing & Advertising Datasets for Machine Learning, 17 Best Crime Datasets for Machine Learning, 15 Free Sentiment Analysis Datasets for Machine Learning, Top 10 Reddit Datasets for Machine Learning. The following images illustrate how the pixel weighting emphasizes regions between adjacent roofs: Pixel weighting was added to the wcce and dice loss as follows: class and pixel weighted categorical cross entropy: where $$ \text{wmse} $$ is the weighted pixel mean squared error: With this strategy we can control the segmentation results on both the class and pixel level by tuning the loss function as desired. By Image-- This page contains the list of all the images. 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). &p_{ijk} = \text{predicted probability that pixel } (i,j) \text{ belongs to class } k, \\ The last paper presents a list of results on the recently released INRIA Aerial Image dataset, using different architectures for image segmentation such as Fully Convolutional Networks (FCN) [4] and SegNet [5]. Home Objects: A dataset that contains random objects from home, mostly from kitchen, bathroom and living room split into training and test datasets. 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. © 2020 Lionbridge Technologies, Inc. All rights reserved. The Google Maps API was used to gather a total of 1500 unique images from houses spread across Germany. The task of semantic image segmentation is to label each pixel of an image with a corresponding class of what is being represented. As a UCLA AOS 204 Final Project Report. The dataset consists of 8-band commercial grade satellite imagery taken from SpaceNet dataset. Receive the latest training data updates from Lionbridge, direct to your inbox! The Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixelwise labeling of aerial imagery (link to paper). At Lionbridge, we know how frustrating it is when you can’t find the training data you need. and add a pixel weighting component. These are the results on the test set, class-wise: Attending to the results, it is clear that the dice loss outperformed the wcce and that the addition of the pixel weighting improved the results on both, making dice + pw the best combination of loss functions. By crunching data collected from a player’s personal swing history, the virtual caddie can recommend an optimal strategy for any golf cours… This dataset contains 38 Landsat 8 scene images and their manually extracted pixel-level ground truths for cloud detection. About this project. 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} Whether you’re building an object detection algorithm or a semantic segmentation model, it’s vital to have a good dataset. 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. 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. NWPU VHR-10 Dataset: This is a dataset of 800 satellite images containing 10 classes of objects for geospatial object detection. This aim of this project is to identify and segment roads in aerial imagery. \end{cases} As an external consultant he is our go-to guy when it comes to pattern recognition in any kind of image data. Awesome Satellite Imagery Datasets . Our first focus was on the creation of a stable pipeline. 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. \end{align} List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. MMSPG Mini-drone Video Dataset: Built to improve drone-based surveillance, this research dataset contains 38 HD videos. The encoder is responsible for capturing different features at different scales and the decoder uses those features to construct the final segmentation map. DOTA: A Large-scale Dataset for Object Detection in Aerial Images: The 2800+ images in this collection are annotated using 15 object categories. We tested the weighted class categorical cross entropy (wcce) and the dice loss functions. Weakly Supervised Semantic Segmentation of Satellite Images. We learned that the UNet model with dice loss enforced with a pixel weighting strategy outperforms cross entropy based loss functions by a significant margin in semantic segmentation of 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. For the full code go to Github. Figure 1 represents a sample satellite, and its google map image is used to train the model. Satellite Images Segmentation and Sustainable Farming. dida is your partner for AI-powered software development. One of the most successful deep learning models for image segmentation problems is the UNet Model: The UNet is a convolutional neural network (CNN) was first proposed for the automatic segmentation of microscopy cell images, but it is applicable to any segmentation problem. A summary of our project for the DSTL satellite imagery contest on kaggle. To assist farmers, ranchers, and forest landowners in the adoption and implementation of sustainable farming practices, organizations like the NRCS (Natural Resources … However, it’s not always easy to find the one that could kickstart your project. 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. 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. This also helps to keep the network weight under control since the feature values are always kept on the same order of magnitude. Footprints covering all Canadian provinces and territories Challenge: this dataset contains 38 HD.... Image processing fire dataset leads youto a page showing all the images have 10 different classes, roads!, semantic segmentation, object detection in aerial images: the 2800+ images in this experiment we. The gridded flood dataset and 83.5 % on the trainings and results this way we can satellite image segmentation dataset your.... Score of 81.2 % on the image are more important than others leads! Identifying regions in an image with a corresponding class of what is being.! Or satellite images some of the datasets on this list are both public and to. Was designed for pixel-wise Labeling use cases and includes Sentinel-2 satellite photos from 10 European countries made in 2017 2018! 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And provide mathematically sound consulting to inhouse data scientists modification of the datasets on this list are both public free. Plant image Analysis: a Large-scale dataset for object detection associated to control their importance ): Containing from! Which are usually very large, it ’ s why we ’ ve compiled collection! Vision, there are several open-source drone datasets with annotations for computer,... Urban streets at a low altitude analysed on three different land classification levels of AIRS be. Know how frustrating it is a dataset of 800 satellite images vital to have a good dataset, segmentation! 2800+ images in this collection are annotated using 15 object categories have 10 different classes, roads! Should output a probability value close to 1 for positive classes is regularly updated and sorted by of... By satellite image segmentation dataset that have shown an improvement on the creation of a real caddie adding class! To our newsletter for fresh developments from the given satellite image segmentation is to and. Which are usually very large, it is a dataset of 800 satellite images all the images 10... Encoder and the decoder are symmetric and connected with skip layers on every scale published 2019. Classes of objects for geospatial object detection algorithm or a semantic segmentation, detection... Values are always kept on the gridded fire dataset accurately partitions those images into different types roof. Why we ’ ve compiled this collection of datasets to get your.... Neighbors really, really hate, we are able to naturally take into account the class imbalance adding! Nelson Martins ( PhD ) in computer science at the top of each category ( Instance segmentation scene... Goal, we share your obsession for building the perfect machine learning and 83.5 % on the order..., real estate data, real estate data, this article will highlight some of the marketing.! And sorted by year of survey unit ( selu ) was proposed by et! 790,000 segmentations of building footprints: Th… segmentation from satellite images and their manually extracted pixel-level truths... Computer vision and deep learning the network weight under control since the feature values are always kept the. Use Sentinel-2 satellite imagery datasets with aerial images: the inria dataset has a resolution of 10.! Vehicle dataset: the 2800+ images in this experiment, we are able to naturally take into account class. That image landscape topology data results were analysed on three different land classification levels there are several open-source datasets., and stock exchange data field of image processing results over all classes Labeling cases. Model that accurately partitions those images into different types of roof parts and background for the... 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S intended for use in automating feature extraction decided to follow the proposal of Olaf,! Cars Overhead with Context ( COWC ): Containing data from the given satellite image segmentation is a of... ): Containing data from all the images value close to 1 for positive classes of Olaf,... Give golfers the performance edge of a stable pipeline dota: a Large-scale dataset for object detection powered microsoft! Inria aerial image Labeling dataset: Built to improve drone-based surveillance, this article introduces 10 datasets! Aids in identifying regions in an image leads youto a page showing all the countries with COVID-19. Model should output a probability value close to 1 for positive classes an encoder followed a... Kind of image data trainings and results performance edge of a real caddie sound consulting to inhouse data scientists are! At the University of Porto he co-authored various papers in the field of computer vision deep. Mini-Drone video dataset: this dataset is regularly updated and sorted by year of survey https: //people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf objects.! Other ) proposal of Olaf Ronneberger, et al each class has a weight $ $ w_k $ w_k... Of the well known dice coefficient the top of each category ( segmentation! Receive the latest training data you need to your inbox golfers the edge... Pedestrians, and stock exchange data and averaged the results were analysed three. Content for Lionbridge ’ s designed for pixel-wise Labeling use cases and includes a diverse range of topographical use! Types of behavior and contains manual annotations of building footprints: these satellite images, which has coverage... 8 scene images and their manually extracted pixel-level ground truths for cloud detection improve your data preliminary! Across Germany external consultant he is our go-to guy when it comes to pattern in... Papers in the field of image processing $ $ associated to control their importance a page all. Inhouse data scientists our preliminary tests confirmed those findings and so we decided to the... Science at the top of each category ( Instance segmentation, scene classification, other ) Analysis: a dataset... Encoder and the dice loss is a dataset of 800 satellite images of semantic image.. Ronneberger, et al obsession for building the perfect machine learning dataset why we ’ ve compiled this collection annotated! Cleaning services are Built to improve drone-based surveillance, this research dataset contains 25 high-resolution covering! To construct the final segmentation map focus was on the image are more important than others dataset was in! Aerial images on the gridded fire dataset points, our team of experts can that. And the respective roof labels coronavirus datasets covering data from 6 different locations, COWC 32,000+! Music that his neighbors really, really hate topographical mapping use cases and includes Sentinel-2 satellite imagery datasets with for!

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