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86 changes: 86 additions & 0 deletions eodag/resources/product_types.yml
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Expand Up @@ -6687,6 +6687,92 @@ MO_OCEANCOLOUR_GLO_BGC_L4_MY_009_108:
missionStartDate: "1997-09-01T00:00:00Z"
missionEndDate: "2024-04-01T00:00:00Z"

# MARK: spacenet ------------------------------------------------------------------------
SPACENET_BUILDINGS_DETECTION_V1:
abstract: |
The first SpaceNet challenge focused on large-scale building detection. It provided 2,544 km² of high-resolution WorldView satellite imagery over Rio de Janeiro, Brazil, along with 382,534 building footprint labels. Both 3-band and 8-band imagery were included to support algorithm development for automated building extraction.
instrument:
platform: SpaceNet
platformSerialIdentifier: SN1
processingLevel:
keywords: Rio,Building,Building Detection v1,Building Detection,v1,SpaceNet,Chipped Training Dataset
sensorType:
license: CC-BY-4.0
title: "SpaceNet 1: Building Detection v1"
missionStartDate: "2016-02-29T00:00:00Z"
missionEndDate:

SPACENET_BUILDINGS_DETECTION_V2:
abstract: |
The SpaceNet 2 dataset expands the building detection challenge to multiple cities worldwide, including Las Vegas, Paris, Shanghai, and Khartoum. It consists of high-resolution WorldView satellite imagery with annotations for more than 300,000 building footprints across 665 km². The dataset is designed to evaluate model generalization and robustness in detecting buildings across diverse urban landscapes.
instrument:
platform: SpaceNet
platformSerialIdentifier: SN2
processingLevel:
keywords: Vegas,Paris,Shanghai,Khartoum,Building,Building Detection v2,Building Detection,v2,SpaceNet,Chipped Training Dataset
sensorType:
license: CC-BY-4.0
title: "SpaceNet 2: Building Detection v2"
missionStartDate: "2015-04-13T00:00:00Z"
missionEndDate:

SPACENET_ROADS_NETWORK_DETECTION:
abstract: |
The SpaceNet 3 dataset was created for the Road Detection and Routing Challenge. It contains more than 8,000 km of road centerlines with detailed attributes, including road type, surface type, and number of lanes. All annotations were digitized from 30 cm GSD WorldView-3 imagery across four cities: Las Vegas, Paris, Shanghai, and Khartoum. This dataset enables the development of algorithms not only for road extraction but also for generating usable routing networks from satellite imagery.
instrument:
platform: SpaceNet
platformSerialIdentifier: SN3
processingLevel:
keywords: Vegas,Paris,Shanghai,Khartoum,Road,Road Network Detection,SpaceNet,Chipped Training Dataset
sensorType:
license: CC-BY-4.0
title: "SpaceNet 3: Road Network Detection"
missionStartDate: "2015-04-13T00:00:00Z"
missionEndDate:

SPACENET_OFF_NADIR_BUILDING:
abstract: |
SpaceNet 4 introduced imagery collected at multiple viewing angles (off-nadir), making building footprint extraction significantly more challenging due to distortions, shadows, and perspective changes. The dataset focused on Atlanta, USA, with labeled building footprints provided across different look angles.
instrument:
platform: SpaceNet
platformSerialIdentifier: SN4
processingLevel:
keywords: Atlanta,Buildings,Chipped Training Dataset,Off-Nadir Buildings,Off-Nadir,v1,SpaceNet
sensorType:
license: CC-BY-4.0
title: "SpaceNet 4: Off-Nadir Buildings"
missionStartDate: "2016-02-29T00:00:00Z"
missionEndDate:

SPACENET_ROADS_NETWORK_ROUTE_TRAVEL:
abstract: |
SpaceNet 5 focuses on automated road network extraction and route travel time estimation from satellite imagery. The publicly available dataset includes high-resolution imagery and road labels for two cities: Moscow, Russia, and Mumbai, India. These annotations enable development of algorithms for extracting road networks and generating connected graphs suitable for routing applications.
instrument:
platform: SpaceNet
platformSerialIdentifier: SN5
processingLevel:
keywords: Moscow,Road,Road Network Extraction,Road Network,Route Travel Time Estimation,Route Travel,SpaceNet
sensorType:
license: CC-BY-4.0
title: "SpaceNet 5: Automated Road Network Extraction and Route Travel Time Estimation from Satellite Imagery"
missionStartDate: "2016-02-29T00:00:00Z"
missionEndDate:

SPACENET_ALL_WEATHER_MAPPING:
abstract: |
SpaceNet 6 (MSAW) introduced multi-sensor data for the first time, combining Synthetic Aperture Radar (SAR) imagery from Capella Space with optical WorldView-2 imagery. The dataset, centered on Atlanta, provides over 48,000 building footprint labels. Training data includes both SAR and optical images, while testing is SAR-only, encouraging robust building detection under all-weather conditions.
instrument: Capella-SAR, WorldView-2
platform: SpaceNet
platformSerialIdentifier: SN6
processingLevel:
keywords: Rotterdam,Buildings,Building Detection,SpaceNet,Multi-Sensor,All-Weather Mapping,SAR,Optical
sensorType:
license: CC-BY-4.0
title: "SpaceNet 6: All-Weather Mapping"
missionStartDate: "2019-08-04T00:00:00Z"
missionEndDate: "2019-08-23T00:00:00Z"


# MARK: GENERIC ------------------------------------------------------------------------
GENERIC_PRODUCT_TYPE:
abstract:
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