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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").

Open spatial dataset - labeling

Datasets

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

Markup classes

Classification for obects labeling in imagery (all classes)

ID CLASS_NAME Description Visual Application domains

0

clutter

Buildings and Construction

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

Oil&Gaz

201

Oil storage facility oil&gas

202

Oil well oil&gas

203

Gas station oil&gas, transport

204

Oil spill oil&gas, ecology

Roads

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

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

Vegetation

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

Water

614

River

615

Lake

616

Swamp

617

Other water body

618

Stone

619

Clay

620

Sand

621

Other soil

622

Sea

Power infrastructure

701

Solar panel

702

Power tower

703

Cell tower

Emergency and risk management

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

Credentials

"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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