A whole bunch of various encoders - SeNets were the most interesting ones.Polygons are ground truth Some other tricks have been tried Model and data processing Nadir=25 and nadir=34 with different azimuths. ![]() Postprocessing with borders mask and watershed.UResNeXt101(UNet + ResNeXt101) with transfer learning.The core of the task - identify all building footprints. The domain - satellite images of Atlanta suburb taken from different look angles( nadirs) separated into three groups: Nadir, Off Nadir, Very Off Nadir. In case you missed last year posts about participation in similar challenges: Spacenet three: Road detector and Crowd AI Mapping challenge Mistakes are easy to make anywhere and how fast you find and fix them defines your chances to win. Maybe the most important thing I have taught during this competition: Leaderboard is the only one truth. Honestly, many things went wrong, but I’ve got an enjoyable and useful experience and managed to improve my skills. Hopefully, the results of this study could be a momentous reference for urban development planning.For me, Spacenet4 became the first serious DL competition. At last, we have used the five models to analyze urbanization processes from 2015 to 2021 in the study area. Experiments have revealed that the STANet-PAM model generally performs the best in detecting the NCAs, and the STANet-PAM model can obtain more detailed information of land changes owing to its pyramid spatial-temporal attention module of multiple scales. ![]() The BiT model is based on transformer, and the others are based on CNN (Conventional Neural Network). In this study, we firstly constructed a high-resolution labels for change detection based on the GF-2 satellite images, and then applied five deep learning models of change detection, including STANets (BASE, BAM, and PAM), SNUNet (Siam-NestedUNet), and BiT (Bitemporal image Transformer) in the Core Region of Jiangbei New Area of Nanjing, China. The advances of remote sensing and deep learning algorithms promotes the high precision of the research work. As a result of the training of 100 epochs with the data set in architectures belonging to Dilated and Attention-Based Networks, IoU values above 0.90 were obtained.Ĭhange detection of the newly constructed areas (NCAs) is important for urban development. After this work, it can be seen that according to several metrics Dilated and Attention-Based Networks perform better than their counterparts. ![]() The final category is Attention-Based Network, in these networks, certain aspects of the data are emphasized while other aspects are ignored. The third category is Dilated Network, due to its atrous structure, which can calculate any layer at any desired resolution, with the presence of holes in the filter. The second category is Feature Pyramid Network, in this type of network scene information is aggregated across pyramid structures which produce more comprehensive results. The first category is Encoder-Decoder Network: an encoder that reduces the spatial resolution of the data and a decoder that recreates the lower resolution result of the encoder and upsamples it. Architectures examined in this work fall under one of few categories. Segmentation results have been obtained using post-FCN architectures. ![]() The imagery is obtained by Pleiades satellite and have a resolution of 0.5 meters. A new building dataset as created consisting of very high-resolution optical satellite images provided by the Center for Satellite Communications and Remote Sensing (CSCRS). Building semantic segmentation is an exceedingly important issue in the field of remote sensing.
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