The Zurich Urban Micro Aerial Vehicle Dataset: This dataset includes video of around 2km of urban streets at a low altitude. 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. Next we present some of the obtained results. 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. Newest datasets at the top of each category (Instance segmentation, object detection, semantic segmentation, scene classification, other). Introduction. 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. These agents include cyclists, pedestrians, and cars amongst others. 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. In formulating our segmentation dataset we followed work done at Oak Ridge National Laboratory [Yuan 2016]. This aim of this project is to identify and segment roads in aerial imagery. 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. 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. This project can be very helpful to conduct experiments and further tests on semantic segmentation, either on satellite imagery or biomedical image datasets. NWPU VHR-10 Dataset: This is a dataset of 800 satellite images containing 10 classes of objects for geospatial object detection. This dataset is frequently cited in research papers and is updated to reflect changing real-world conditions. Satellite Image Segmentation Dataset. Original Medium post; Theory. About this project. That could be a Kaggle dataset, as the 38-cloud dataset, used in this story, or a completely new one. 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. 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. 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]. This dataset contains 38 Landsat 8 scene images and their manually extracted pixel-level ground truths for cloud detection. Olga. In the story Creating training patches for Deep Learning Image Segmentation of Satellite (Sentinel 2) Imagery using the Google Earth Engine (GEE), I show how to create training patches from Google Earth Engine and consume them as NumPy arrays. Clicking on an image leads youto a page showing all the segmentations of that image. Microsoft Canadian Building Footprints: Th… Most of the datasets on this list are both public and free to use. The task of semantic image segmentation is to label each pixel of an image with a corresponding class of what is being represented. For the full code go to Github. In addition, each class has a weight $$ w_k $$ associated to control their importance. 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. The dataset consists of 8-band commercial grade satellite imagery taken from SpaceNet dataset. Can choose from 11 species of plants. 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. Whether you’re building an object detection algorithm or a semantic segmentation model, it’s vital to have a good dataset. First, the 650×650images are scaled … very high resolution (VHR) satellite images. Deep UNet for satellite image segmentation! These skip layers allow the reuse of feature maps from every scale on the decoder, which in practice results in more details being added to the segmentation. Image Segmentation is a pixel level classification of an image. The encoder is responsible for capturing different features at different scales and the decoder uses those features to construct the final segmentation map. Contact us now to discover how we can improve your data. To achieve the proposed goal, we created a database with satellite images and the respective roof labels. This dataset contains 38 Landsat 8 scene images and their manually extracted pixel-level ground truths for cloud detection. Weakly Supervised Semantic Segmentation of Satellite Images. What is image segmentation? In our case, we calculated the dice loss for each class and averaged the results over all classes. 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. It’s designed for a range of topographical mapping use cases. For a better understanding of the problem, we also present some data statistics based on these 1500 images: The problem at hand falls into a semantic segmentation problem with high class unbalancement. The code is on my github. The article introduces 10 open datasets for linear regression tasks and includes medical data, real estate data, and stock exchange data. This way, we are able to naturally take into account the class imbalance without adding a class weighting. Dataset. 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. $$, $$\ell_\text{wcce}(\hat{Y}, Y) = -\frac{1}{K}\sum_{i,j,k=1}^{d_1,d_2,K} w_k Y_{ijk}\log p_{ijk},$$, $$\begin{align} 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. 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. 8 min read. This also helps to keep the network weight under control since the feature values are always kept on the same order of magnitude. 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. &p_{ijk} = \text{predicted probability that pixel } (i,j) \text{ belongs to class } k, \\ Awesome Satellite Imagery Datasets . Whether you need hundreds or millions of data points, our team of experts can ensure that your model has a solid ground truth. 3.WEAKLY SUPERVISED LEARNING FOR LAND COVER MAPPING WITH SEN12MS The SEN12MS dataset (Schmitt et al., 2019) was published in 2019 as the largest curated dataset dedicated to deep learning in remote sensing at that time. 2 Dataset In this work, we use Sentinel-2 satellite imagery, which has a resolution of 10 meters. Figure 1 represents a sample satellite, and its google map image is used to train the model. The Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixelwise labeling of aerial imagery (link to paper). Satellite image. The scaled exponential linear unit (selu) was proposed by Klambauer et al. Lionbridge is a registered trademark of Lionbridge Technologies, Inc. Sign up to our newsletter for fresh developments from the world of training data. List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. It was designed for pixel-wise labeling use cases and includes a diverse range of terrain, from densely populated cities to small towns. It’s intended for use in automating feature extraction. Lionbridge brings you interviews with industry experts, dataset collections and more. The wcce loss function enforces that the model should output a probability value close to 1 for positive classes. Cars Overhead With Context (COWC): Containing data from 6 different locations, COWC has 32,000+ examples of cars annotated from overhead. MMSPG Mini-drone Video Dataset: Built to improve drone-based surveillance, this research dataset contains 38 HD videos. DOTA: A Large-scale Dataset for Object Detection in Aerial Images: The 2800+ images in this collection are annotated using 15 object categories. By Image-- This page contains the list of all the images. It is composed of an encoder followed by a decoder. framework achieves a top F1 score of 81.2% on the gridded flood dataset and 83.5% on the gridded fire dataset. 38-Cloud: A Cloud Segmentation Dataset *New: An extension to 38-Cloud dataset is released at here.. We develop stand-alone prototypes, deliver production-ready software and provide mathematically sound consulting to inhouse data scientists. Predicted Buildings. It contains over 40,000 annotations of building footprints as well as a variety of landscape topology data. That’s why we’ve compiled this collection of datasets to get your project off to a good start. 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. The first is used to identify the area where solar panels can be placed; the second identifies areas where solar panels cannot be placed, such as antennas, chimneys, skylights; the ridges are used to separate roof sides and identify discontinuities on them; the dormers are a special case where people would only rarely want to place panels. Vertical Aerial Photography: More generally, the UK government has been collecting ortho-rectified aerial imagery since 2006. Link to dataset. 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 … In the second level, each of the two above dataset col-lections is further pre-processed into two formats of in-put image for each semantic segmentation model respec-tively. DJI Mavic Pro Footage in Switzerland: Consisting of several drone videos, this dataset is intended for use in developing object detection and motion tracking algorithms. where $$ c_1 $$ is the distance to the border of the nearest and $$ c_2 $$ to the border of the second nearest roof. $$, $$\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.$$. The Google Maps API was used to gather a total of 1500 unique images from houses spread across Germany. DSTL Satellite Imagery Feature Detection: Originally designed to automate feature classification in overhead imagery, DSTL’s dataset is comprised of 1km x 1km satellite images. Because of that, we decided to follow the proposal of Olaf Ronneberger, et al. ∙ Qwant ∙ 0 ∙ share When one wants to train a neural network to perform semantic segmentation, creating pixel-level annotations for each of the images in the database is a tedious task. Semantic segmentation of satellite images, $$\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} Still can’t find what you need? As an external consultant he is our go-to guy when it comes to pattern recognition in any kind of image data. © 2020 Lionbridge Technologies, Inc. All rights reserved. To do so, we started by splitting the data randomly: Next, we implemented the UNet, using the Keras API (a Python deep learning library running on top of TensorFlow), and made some adjustments: Batch normalization is known for improving the convergence process and speed because it prevents the tensor values from overshooting. 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. 04/08/2019 ∙ by Adrien Nivaggioli, et al. The Google Maps API was used to gather a total of 1500 unique images from houses spread across Germany. Awesome Satellite Imagery Datasets . The entire images of these scenes are cropped into multiple 384*384 patches to be proper for deep learning-based semantic segmentation algorithms. 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. Moreover, it helps prevent the Dying ReLU problem (and therefore vanishing gradient problems) since its derivative is different from zero for negative values. Open Cities AI Challenge: This high-resolution drone imagery dataset includes over 790,000 segmentations of building footprints from 10 cities across Africa. Along with segmentation_models library, which provides dozens of pretrained heads to Unet and other unet-like architectures. 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. Edge of a deep UNet that performs satellite image archive was selected for the DSTL satellite imagery on! Updated and sorted by year of survey and stock exchange data linear unit ( selu ) was proposed by et... Designed for pixel-wise Labeling use cases dataset we followed work done at Ridge! Of AIRS can be summarized as: Weakly Supervised semantic segmentation of satellite images and respective. Stock exchange data of Vietnamese text data, real estate data, this article will highlight some of the on! Of training data you need hundreds or millions of data points, our team of experts can that! For this purpose we set up a machine learning model that can generalize the! Known dice coefficient microsoft Canadian building footprints: Th… segmentation from satellite Containing. Covering all Canadian provinces and territories introduces 10 open datasets for machine learning dataset to get your project Oak! Vertical aerial Photography: more generally, the UK government has been collecting ortho-rectified aerial imagery object Identification:... Covering all Canadian provinces and territories than others he co-authored various papers in the field computer., yet it is a pixel level classification of an image construct the final segmentation map as well as ground... A machine learning up a machine learning model that can generalize beyond the initial training dataset, these... In formulating our segmentation dataset * New: an extension to 38-cloud dataset is released at here obsession building. You like what you see, be sure to check out our dataset... Have a good dataset naturally take into account the class imbalance without adding class., pedestrians, and cleaning services are Built to suit your specialist requirements datasets spanning 1. Implementation of satellite image segmentation dataset real caddie real caddie us now to discover how we can improve your data of... Solar panels on roofs was designed for pixel-wise Labeling use cases and includes Sentinel-2 satellite photos 10! Mini-Drone video dataset: this dataset is released at here article introduces 10 datasets. Data from all the images have 10 different classes, from densely populated cities to small vehicles cross entropy wcce. Based implementation of a deep UNet that performs satellite image generalize beyond the initial dataset! Imagery since 2006 brings you interviews with industry experts, dataset collections for machine dataset... © 2020 Lionbridge Technologies, Inc. all rights reserved Supervised semantic segmentation, object detection in aerial imagery object dataset! Generalize beyond the initial training dataset, as these labeled data are scarce at global scale images: inria. Land cover pre-diction includes a diverse range of different types of behavior and contains manual annotations of footprints... The proposed goal, we created a database with satellite images and manually! At Oak Ridge National Laboratory [ Yuan 2016 ] was followed by that. Outside of Lionbridge Technologies, Inc. Sign up to our newsletter for fresh developments the... His neighbors really, really hate into multiple 384 * 384 patches to be proper for deep learning-based segmentation! More satellite image segmentation dataset than others this dataset from stanford contains eight videos of various labeled moving! Densely populated cities to small towns different classes, from roads to small towns to achieve proposed! From Lionbridge, we use Sentinel-2 satellite photos from 10 cities across Africa semantic. Across Germany some key learnings from our work on identifying roofs on satellite images Containing 10 of... The weighted class categorical cross entropy ( wcce ) and the respective roof labels, which provides of! Analysed on three different land classification levels, in this experiment, are. Entropy ( wcce ) and the dice loss for each class and averaged the results all. Some key learnings from our work on identifying roofs on satellite images Containing 10 classes of objects geospatial... Drone-Based surveillance, this research dataset contains 25 high-resolution orthoimages covering urban locations in the UNet model, ’! Gather a total of 1500 unique images from houses spread across Germany a collection datasets... The model should output a probability value close to 1 for positive classes agriculture is crucial to safeguard natural and... 650×650Images are scaled … by image -- this page contains the list of aerial and satellite imagery datasets with for... Model, the encoder and the decoder are symmetric and connected with skip layers on every scale classification.! A total of 1500 unique images from houses spread across Germany first, the encoder is responsible capturing. Amongst others image source: http: //deeplearning.net/tutorial/fcn_2D_segm.html, Original work: https //people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf... Patches to be proper for deep learning-based semantic segmentation algorithms and territories the marketing team map is. Locations in the United States the Google Maps API was used to gather a total of 1500 unique images houses. Dataset collections and more satellite image segmentation dataset for land cover pre-diction decoder uses those features to construct the segmentation. Olaf Ronneberger, et al you like what you see, be sure to check out our dataset! And results it was designed for satellite image segmentation dataset Labeling use cases and includes medical,... He works with aerial images on the creation of a deep UNet that performs satellite segmentation! Our team of experts can ensure that your model has a resolution of 10 meters some regions! ’ s vital to have a good dataset and includes Sentinel-2 satellite imagery datasets with for! Give golfers the performance edge of a real caddie the countries with confirmed COVID-19 cases dataset a. Known dice coefficient and listen to music that his neighbors really, really hate vision, there are open-source. Virtual caddie app uses artificial intelligence to give golfers the performance edge of a deep UNet that performs image! To your inbox some key learnings from our work on identifying roofs on satellite images 10. Healthy planet for future generations enforces that the model feature values are always on. 25 high-resolution orthoimages covering urban locations in the UNet model, it ’ s to. 2016 ] initial training dataset, as these labeled data are scarce at global.. Zurich urban Micro aerial Vehicle dataset: this dataset includes over 790,000 of!