- SPACENET BUILDINGLABELS EXAMPLE DRIVERS
- SPACENET BUILDINGLABELS EXAMPLE PATCH
- SPACENET BUILDINGLABELS EXAMPLE FULL
- SPACENET BUILDINGLABELS EXAMPLE ZIP
The motivation is to reduce the impact of uncertain border definitions on the evaluation. Those eroded areas are then ignored during evaluation.
SPACENET BUILDINGLABELS EXAMPLE FULL
Besides the full reference, we also prepared references where the boundaries of objects are eroded by a circular disc of 3 pixel radius. Per tile we use a reference classification which was produced in the same manner as the reference tiles we provide for training. Emails with attachments larger than 15MB will not be processed. This zip-file, together with a detailed description of the method used (or reference to a published paper) should be sent to Markus Gerke. Such a zip-file containing all the resulting tif-label files is typically not larger than 10 MB.
SPACENET BUILDINGLABELS EXAMPLE ZIP
→ DO NOT USE ANY TIF compression format, not even lossless INSTEAD zip all original tif-files into one zip file. The tif label files must have the same size as the original tile. top_mosaic_09cm_area8_class.tif, or top_mosaic_09cm_area38_class.tif. Top_mosaic_09cm_areaXX_class.tif, where XX is the number of the patch, e.g. The naming convention for tif files containing the label information is derived from the provided files: Validation of provided reference is not done and if delivered, the data will be ignored.
SPACENET BUILDINGLABELS EXAMPLE PATCH
For instance, it is not possible to submit only classification results for the category building.Ī full classification of each patch for which we do not provide ground truth is expected. The clutter/background class includes water bodies (present in two images with part of a river) and other objects that look very different from everything else (e.g., containers, tennis courts, swimming pools) and that are usually not of interest in semantic object classification in urban scenes, however note that participants must submit labels for all classes (including the clutter/background class). Six categories/classes have been defined: Participants shall use all data with ground truth for training or internal evaluation of their method. We provide the classification data (label images) for approximately half of the images, while the ground truth of the remaining scenes will remain unreleased and stays with the benchmark test organizers to be used for evaluation of submitted results. While Vaihingen is a relatively small village with many detached buildings and small multi story buildings, Potsdam shows a typical historic city with large building blocks, narrow streets and dense settlement structure.Įach dataset has been classified manually into six most common land cover classes. To this end we provide two state-of-the-art airborne image datasets, consisting of very high resolution treue ortho photo (TOP) tiles and corresponding digital surface models (DSMs) derived from dense image matching techniques.
This "semantic labeling contest" of ISPRS WG III/4 is meant to resolve this issue. One major problem that is hampering scientific progress is a lack of standard data sets for evaluating object extraction, so that the outcomes of different approaches can hardly be compared experimentally. To our knowledge, no fully automated method for 2D object recognition is applied in practice today although at least two decades of research have tried solving this task. Despite the enormous efforts spent, these tasks cannot be considered solved, yet.
SPACENET BUILDINGLABELS EXAMPLE DRIVERS
Further research drivers are very high-resolution data from new sensors and advanced processing techniques that rely on increasingly mature machine learning techniques. Focus is on detailed 2D semantic segmentation that assigns labels to multiple object categories. What makes this task challenging is the very heterogeneous appearance of objects like buildings, streets, trees and cars in very high-resolution data, which leads to high intra-class variance while the inter-class variance is low. One of the major topics in photogrammetry is the automated extraction of urban objects from data acquired by airborne sensors.
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