About this project. $$, $$\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.$$. It’s designed for a range of topographical mapping use cases. segmentation model that can generalize beyond the initial training dataset, as these labeled data are scarce at global scale. Image Segmentation is a pixel level classification of an image. 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. 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 aim of this project is to identify and segment roads in aerial imagery. As a UCLA AOS 204 Final Project Report. Daniel writes a variety of content for Lionbridge’s website as part of the marketing team. 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. 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. Moreover, it helps prevent the Dying ReLU problem (and therefore vanishing gradient problems) since its derivative is different from zero for negative values. Outside of Lionbridge, he loves to travel, take photos and listen to music that his neighbors really, really hate. 0 & \text{if pixel } (i,j) \text{ does not belong to class } k. Link to dataset. Awesome Satellite Imagery Datasets . 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 Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixelwise labeling of aerial imagery (link to paper). Clicking on an image leads youto a page showing all the segmentations of that image. Contact us now to discover how we can improve your data. Weakly Supervised Semantic Segmentation of Satellite Images. In addition, each class has a weight $$ w_k $$ associated to control their importance. 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. Open Cities AI Challenge: This high-resolution drone imagery dataset includes over 790,000 segmentations of building footprints from 10 cities across Africa. 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. Our array of data creation, annotation, and cleaning services are built to suit your specialist requirements. 04/08/2019 ∙ by Adrien Nivaggioli, et al. DOTA: A Large-scale Dataset for Object Detection in Aerial Images: The 2800+ images in this collection are annotated using 15 object categories. Whether you need hundreds or millions of data points, our team of experts can ensure that your model has a solid ground truth. The code is on my github. It was designed for pixel-wise labeling use cases and includes a diverse range of terrain, from densely populated cities to small towns. 38-Cloud: A Cloud Segmentation Dataset *New: An extension to 38-Cloud dataset is released at here.. 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). Detection in aerial images: the inria dataset has a coverage of square! Focus was on the same order of magnitude of a real caddie key learnings from work! 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