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Massachusetts Buildings Dataset

15111
Tagsatellite, aerial
Taskinstance segmentation
Release YearMade in 2013
Licenseunknown

Introduction #

Volodymyr Mnih

The Massachusetts Buildings Dataset comprises 151 aerial images of the Boston area, each with a resolution of 1500 Γ— 1500 pixels, covering an area of approximately 2.25 square kilometers. This results in a dataset that encompasses roughly 340 square kilometers in total. To create subsets, the data was divided into a train set with 137 images, a test set with 10 images, and a val set with 4 images. The target maps were generated by converting building footprints obtained from the OpenStreetMap project into rasterized forms. Unlike the GTA Buildings dataset used elsewhere in the thesis, this dataset was confined to regions with an average omission noise level of approximately 5% or less. The large volume of high-quality building footprint data was collected because the City of Boston contributed building footprints for the entire city to the OpenStreetMap project. The dataset predominantly represents urban and suburban areas and includes buildings of all sizes, from individual houses to garages.

Fig

Figures (a) and (b) show two representative regions from the Massachusetts Buildings dataset. Figures Β© and (d) show predictions of a postprocessing network on these regions. Green pixels are true positives, red pixels are false positives, blue pixels are false negatives, and background pixels are true negatives.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkKaggle

Summary #

Massachusetts Buildings Dataset 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 151 images with 211791 labeled objects belonging to 1 single class (building).

Images in the Massachusetts Buildings Dataset 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 3 splits in the dataset: train (137 images), test (10 images), and val (4 images). The dataset was released in 2013 by the University of Toronto.

Dataset Poster

Explore #

Massachusetts Buildings Dataset dataset has 151 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.

OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
OpenSample annotation mask from Massachusetts Buildings DatasetSample image from Massachusetts Buildings Dataset
πŸ‘€
Have a look at 151 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.

Search
Rows 1-1 of 1
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
buildingβž”
mask
151
211791
1402.59
13.48%

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.

Search
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
211791
0.01%
8.4%
0%
1px
0.07%
662px
44.13%
17px
1.1%
2px
0.13%
547px
36.47%

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.

Spatial Heatmap

Objects #

Table contains all 98727 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.

Search
Rows 1-10 of 98727
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
building
mask
23878945_15.png
1500 x 1500
10px
0.67%
10px
0.67%
0%
2βž”
building
mask
23878945_15.png
1500 x 1500
22px
1.47%
26px
1.73%
0.02%
3βž”
building
mask
23878945_15.png
1500 x 1500
7px
0.47%
12px
0.8%
0%
4βž”
building
mask
23878945_15.png
1500 x 1500
8px
0.53%
7px
0.47%
0%
5βž”
building
mask
23878945_15.png
1500 x 1500
9px
0.6%
9px
0.6%
0%
6βž”
building
mask
23878945_15.png
1500 x 1500
17px
1.13%
16px
1.07%
0.01%
7βž”
building
mask
23878945_15.png
1500 x 1500
16px
1.07%
15px
1%
0.01%
8βž”
building
mask
23878945_15.png
1500 x 1500
9px
0.6%
8px
0.53%
0%
9βž”
building
mask
23878945_15.png
1500 x 1500
14px
0.93%
8px
0.53%
0%
10βž”
building
mask
23878945_15.png
1500 x 1500
17px
1.13%
14px
0.93%
0.01%

License #

License is unknown for the Massachusetts Buildings dataset.

Source

Citation #

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

@phdthesis{MnihThesis,
  author = {Volodymyr Mnih},
  title = {Machine Learning for Aerial Image Labeling},
  school = {University of Toronto},
  year = {2013}
}

Source

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

@misc{ visualization-tools-for-massachusetts-buildings-dataset,
  title = { Visualization Tools for Massachusetts Buildings Dataset Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/massachusetts-buildings } },
  url = { https://datasetninja.com/massachusetts-buildings },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Please visit dataset homepage to download the data.

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