Here is presented the list of open datasets created by Aeronetlab group at Skoltech for objects recognition in satellite and aerial images. Most of datasets are distributed under the Open License within a single pipeline supported by a data access tools (check for Aeronetlib in our github page). These experimental datasets are to be used in training or validation of the deep learning algorithms.
Despite of increasing number of datasets and competitions in remote sensing data science and some large datasets that'd been provided to the research community (e.g. Spacenet) there is still a lack of geographical diversity and the number of training classes. The dataset is proposed to be extended to different data sources, territories and application domains in accordance with classification of the natural and man-made objects that have a clear interpretation either in satellite or in aerial imagery (see "markup classes").
| Project name | Number of datasets | Description | Size | Dwonload link |
|---|---|---|---|---|
| "Emergenсy mapping" | 2 | Emergency Mapping is a deeplearning method to detect destroyed (damaged) buildings in remote sensing imagery | 235 Mb + 5.2 Gb | Download |
| "Building heights" | 1 | For validation of buldings heights using the method for heights reconstruction in single image by the known sun and satellite angles | 1.2 Gb | Download |
Classification for obects labeling in imagery (all classes)
| ID | CLASS_NAME | Description | Visual | Application domains |
0 |
clutter | |||
101 |
Residential building | Roofs (not footprints!) of apartment buildings. Shoud have 5+ storeys. | 
|
retail, real estate, urban, mapping |
901 |
building shadow | Shadows of multi-storey buildings. Should be related to appropiate building | 
|
retail, real estate, urban |
902 |
building wall | Walls of multi-storey buildings | 
|
retail, real estate, urban |
102 |
House | 
|
retail, real estate, urban | |
103 |
Industrial building | Plants, etc. | 
|
retail, urban |
104 |
Commercial building | Usualy hard to define only by imagery | retail, real estate, urban | |
105 |
Other non-residential buildings | Garages, hangars, etc. - mostly small non-residential buildings | ||
106 |
Construction site | The site wherer construction work is going | retail, construction, real estate | |
107 |
Construction building | 
|
construction | |
108 |
Pit | construction | ||
109 |
Swimming Pool | Swimming pool at the private residential area. Shoud have a relation with private house | retail, marketing | |
110 |
Religious | |||
201 |
Oil storage facility | 
|
oil&gas | |
202 |
Oil well | 
|
oil&gas | |
203 |
Gas station | 
|
oil&gas, transport | |
204 |
Oil spill | 
|
oil&gas, ecology | |
301 |
Highway | 
|
transport | |
302 |
Track | 
|
transport | |
303 |
Footpath | transport, tourism | ||
304 |
Railway | 
|
transport | |
305 |
Bridge | transport, marine | ||
306 |
Parking lot | 
|
transport | |
308 |
Cross walk | urban, transport | ||
307 |
Other road | transport | ||
309 |
Highway and tracks | Union of 301 and 302 classes | transport | |
401 |
Train | transport | ||
402 |
Truck | transport | ||
403 |
Car | transport | ||
404 |
Vessel | transport, marine | ||
405 |
Airplane | transport | ||
406 |
Other vehicle | transport | ||
501 |
Dock | marine | ||
502 |
Container | 
|
marine, transport | |
601 |
Tree | |||
602 |
No leaf tree | |||
603 |
Forest | |||
604 |
Low forest | Low or coppice forest | ||
605 |
Palm tree | |||
606 |
Other tree | |||
607 |
Shrub | Shrubland | ||
608 |
Plough | |||
609 |
Сrops | |||
610 |
Lawn | |||
611 |
Grassland | |||
612 |
Other low vegetation | |||
613 |
Woodlands | 
|
||
614 |
TSV | Union for 603, 604, 606, 607 and 613 classes | ||
614 |
River | |||
615 |
Lake | |||
616 |
Swamp | |||
617 |
Other water body | |||
618 |
Stone | |||
619 |
Clay | |||
620 |
Sand | |||
621 |
Other soil | |||
622 |
Sea | |||
701 |
Solar panel | |||
702 |
Power tower | |||
703 |
Cell tower | |||
801 |
Destroyed building | 
|
||
802 |
Damaged building | 
|
||
803 |
Landfill | |||
804 |
Flooded area | Flooded residential area whrere the water poses a threat to locals | ||
805 |
Flooded residential area | |||
806 |
Forest loss | Forest losse due to wildfires, loggings etc. | ||
807 |
Forest growth | Forest growth inverse to forest losses | ||
808 |
Changes of residental buildings | |||
"Open datasets" is the joint project of Skoltech and University of Innopolis, maintained by AeronetLab at Skoltech. The goal of the project is to provide research and developers community with training datasets and benchmarks to develop deep learning algorithms for Earth Observation data analysis.
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