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Department: Photogrammetry and Image Analysis
Earth Observation Center
Department: Atmospheric Processors
Department: EO Data Science
Department: Experimental Methods
Department: Photogrammetry and Image Analysis
Team: Projekte und Missionen
Team: 3D and Modeling
Team: Hyperspectral Remote Sensing
Team: Traffic Monitoring
Team: Optical Remote Sensing of Water (BA)
Department: SAR Signal Processing
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The following public datasets have been created to be used in various research publications. Please quote the specified publications when using the datasets.
SMARS – A Simulated Multimodal Aerial Remote Sensing dataset
SMARS is a large synthetic dataset containing pairs of scenes with simulated urban changes aimed at training and validating change detection applications, as well as urban segmentation and building extraction tasks. In order to cover different contexts, the dataset is modeled on the topography of two European cities: Paris and Venice, resulting in two pairs of scenes named as SParis and SVenice, respectively, with associated orthoimages and Digital Surface Models (DSMs).
EAGLE: Dataset for vehicle detection in real scenarios based on aerial images
The automated recognition of different vehicle classes and their orientation on aerial images is an important task in the field of traffic research and also finds applications in disaster management, among other things. For the further development of corresponding algorithms that deliver reliable results not only under laboratory conditions but also in real scenarios, training data sets that are as extensive and versatile as possible play a decisive role. For this purpose, we present our dataset EAGLE (oriEnted vehicle detection using Aerial imaGery in real-worLd scEnarios).
XWHEEL dataset for vehicle detection in the Global South from aerial imagery
Automatic vehicle detection from aerial imagery is of interest for various applications such as traffic management, parking surveillance, urban planning and emission calculation. Currently, most of the datasets available for vehicle detection algorithms are based on images of the global North. As a result, existing algorithms are adapted to the conditions of the North and are of limited use in the Global South due to differences in, for example, total number and types of vehicles.
DLR HySU (HyperSpectral Unmixing) dataset
The DLR HySU (HyperSpectral Unmixing) dataset provides a publicly available benchmark to assess the performance of spectral unmixing algorithms. The dataset consists of airborne data acquired by a HySpex imaging spectrometer and a 3K RGB camera system over DLR premises at Oberpfaffenhofen, complemented by in-situ spectra recorded with an SVC field spectrometer.
DLR-SkyScapes: Aerial Semantic Segmentation Dataset for HD-mapping
High-Definition(HD) mapping is in many applications from autonomous driving to infrastructure monitoring, and urban management essential for the understanding of complex urban infrastructure with centimeter-level accuracy. Aerial images provide valuable information over a large area instantaneously; nevertheless, no current dataset captures the complexity of aerial scenes at the level of granularity required by real-world applications. To address this, we introduce SkyScapes, an aerial image dataset with highly-accurate, fine-grained annotations for pixel-level semantic labeling.
DLR’s Aerial Crowd Dataset (DLR-ACD)
The DLR-ACD dataset is a collection of aerial images for crowd counting and density estimation, as well as for person localization at mass events.
DLR Multi-class Vehicle Detection and Orientation in Aerial Imagery (DLR-MVDA)
DLR-MVDA is an open aerial image dataset with annotated vehicles. The automatic detection of vehicles from aerial imagery is of interest for various applications such as traffic management, parking surveillance, and urban planning.
Enhancing Road Maps by Parsing Aerial Images around the World (ERM-PAIW)
In this dataset, we provide the image characteristics used and the source code of the method. With this methodology, freely available worldwide road maps, such as OpenStreetMap, are supplemented and improved by automated analysis of aerial photographs.
Fine-grained Road Segmentation by Parsing Ground and Aerial Images (HD-Maps)
HD-Maps is a dataset used in our research for proposing a method which enriches road maps by including spatially high-resolution features such as the number and width of lanes, sidewalks, and parking lanes.
Multi-Sensor Land-Cover Classification (MSLCC)
The MSLCC dataset has been created for multi-sensor land-cover classification, and has been used and published in . It includes multispectral and SAR images acquired by Sentinel-1B and Sentinel-2A for two cities in Germany (Munich and Berlin) and their surrounding areas.
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