Alabama Buildings Segmentation

Introduction #

Duy Cao

Alabama Buildings Segmentation dataset is the combination of BingMap satellite images and masks from Microsoft Maps. It is almost from Alabama, US (99%). Others from Columbia. Dataset contains 10200 satellite images and 10200 masks with weight ~ 17Gb. The satellite images from this dataset have resolution 0.5m/pixel, image size 1024x1024, ~1.5Mb/image. Dataset only contains pictures that have the total area of builbuilding in mask >= 1% area of that pictures. It means there are no images that do not have any building in this dataset.

Please note, that we detected some wrong masks (f.e. ‘10.png’).

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Summary #

Alabama Buildings Segmentation is a dataset for instance segmentation, semantic segmentation, and object detection tasks. Possible applications of the dataset could be in the geospatial domain and environmental industry.

The dataset consists of 10200 images with 657483 labeled objects belonging to 1 single class (building).

Images in the Alabama Buildings Segmentation dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2022.

Explore #

Alabama Buildings Segmentation dataset has 10200 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

Sample annotation mask from Alabama Buildings Segmentation

Sample image from Alabama Buildings Segmentation

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Have a look at 10200 images

Because of dataset's license preview is limited to 12 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
building➔ mask	10200	657483	64.46	5.88%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-1 of 1

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
building mask	657483	0.09%	45.32%	0%	1px	0.1%	1013px	98.93%	34px	3.28%	1px	0.1%	1024px	100%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 102047 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 102047

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	building mask	10189.png	1024 x 1024	3px	0.29%	6px	0.59%	0%
2➔	building mask	10189.png	1024 x 1024	1px	0.1%	10px	0.98%	0%
3➔	building mask	10189.png	1024 x 1024	14px	1.37%	27px	2.64%	0.02%
4➔	building mask	10189.png	1024 x 1024	13px	1.27%	26px	2.54%	0.03%
5➔	building mask	10189.png	1024 x 1024	22px	2.15%	25px	2.44%	0.02%
6➔	building mask	10189.png	1024 x 1024	17px	1.66%	23px	2.25%	0.04%
7➔	building mask	10189.png	1024 x 1024	8px	0.78%	23px	2.25%	0.02%
8➔	building mask	10189.png	1024 x 1024	31px	3.03%	31px	3.03%	0.05%
9➔	building mask	10189.png	1024 x 1024	33px	3.22%	38px	3.71%	0.06%
10➔	building mask	10189.png	1024 x 1024	25px	2.44%	26px	2.54%	0.03%

License #

License is unknown for the Alabama Buildings Segmentation dataset.

Source

Citation #

If you make use of the Alabama Buildings Segmentation data, please cite the following reference:

@dataset{Alabama Buildings Segmentation,
  author={Duy Cao},
  title={Alabama Buildings Segmentation},
  year={2022},
  url={https://www.kaggle.com/datasets/meowmeowplus/alabama-buildings-segmentation}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-alabama-buildings-segmentation-dataset,
  title = { Visualization Tools for Alabama Buildings Segmentation Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/alabama-buildings-segmentation } },
  url = { https://datasetninja.com/alabama-buildings-segmentation },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Please visit dataset homepage to download the data.

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