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199 Datensätze

Luft- und Raumfahrt

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Vom Luftfahrt-Bundesamt durchgeführte Drogenkontrollen

Vom Luftfahrt-Bundesamt durchgeführte Drogenkontrollen.

Luft- und Raumfahrt
Bereitgestellt durch

Luftfahrt-Bundesamt (LBA)

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

29.04.2021

Zeitbezug der Daten

01.01.2017 — 31.12.2020

Aktualisierungsfrequenz

Jährlich

Raumbezug

Vom Luftfahrt-Bundesamt durchgeführte Alkoholkontrollen

Vom Luftfahrt-Bundesamt durchgeführte Alkoholkontrollen.

Luft- und Raumfahrt
Bereitgestellt durch

Luftfahrt-Bundesamt (LBA)

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

29.04.2021

Zeitbezug der Daten

01.01.2017 — 31.12.2020

Aktualisierungsfrequenz

Jährlich

Raumbezug

Umfragedaten Akzeptanz von Urban Air Mobility, insb. Flugtaxis

Hierbei handelt es sich um Umfragedaten zur Akzeptanz von Urban Air Mobility, die im Projekt GABi erhoben wurden. Die Studie wurde im Herbst 2019 durchgeführt. Der inhaltliche Fokus lag auf der individuellen Akzeptanz von Flugtaxis. Die einzelnen Items sind im Datensatz beschriftet und sollten selbsterklärend sein. Zur Anonymisierung wurden die demografischen Informationen aus dem Datensatz entfernt.

mFUND-Projekt: GABi, FKZ: 45UAS1011A

Luft- und Raumfahrt
Bereitgestellt durch

Katholische Universität Eichstätt-Ingolstadt

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

24.03.2021

Zeitbezug der Daten

19.09.2019 — 20.09.2019

Aktualisierungsfrequenz

Niemals

Raumbezug

AuDeRi Datensätze

Problemstellung

Georisiken wie Erdfälle können ein Risiko für die Sicherheit der Bevölkerung darstellen und Schäden an öffentlichem und privatem Eigentum verursachen. Herkömmliche Methoden zur Entdeckung von Erdfällen sind in relevanten Gebieten teils schwer durchführbar. Gefährdete Bereiche sind unter Umständen schwer zugänglich sowie zu weitläufig. Bisher existiert keine effiziente, automatisierbare Methode zur Früherkennung von Erdfällen. Unbemannte Luftfahrtsysteme (UAS) können hier neue Möglichkeiten bieten.

Projektziel

Verschiedene mit UAS einsetzbare Methoden werden hinsichtlich ihrer Tauglichkeit zur Früherkennung von Erdfällen evaluiert. Für das jeweilige Potential von Aufnahmen wird in zwei Testgebieten eine aussagekräftige Datenbasis geschaffen. In der Forschung erprobte Methoden werden mit dem bereits gängigen Einsatz von UAS kombiniert und so in die Praxis übertragen. Aus den gewonnenen Daten werden für das Landesamt für Geologie und Bergbau Rheinland-Pfalz Gefahrenhinweiskarten der Testgebiete erstellt, um Sicherungsmaßnahmen zu ermöglichen.

Durchführung

Es werden großflächige UAS-Befliegungen mit thermalen und kurzwelligen Infrarot-Sensoren, RGB- und Hyperspektral-Kameras in den zwei Risikogebieten durchgeführt. Zur Kampagne gehören Befliegungen zu verschiedenen Jahreszeiten. Dabei soll ermittelt werden, ob verborgene Karst- und Bergbauhohlräume durch ihre thermische Signatur erfasst werden können.

mFUND-Projekt: AuDeRi

Luft- und Raumfahrt
Bereitgestellt durch

Karlsruher Institut für Technologie (KIT)

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

12.03.2021

Zeitbezug der Daten

01.10.2019 — 01.04.2020

Raumbezug

fCover - Sentinel-2 - Netherlands, 2016

The Sentinel-2 fractional vegetation cover (fCover) product for the Netherlands was produced as part of the NextGEOSS project at the German Aerospace Center (DLR). The goal is to derive abundance maps from atmospherically corrected Sentinel-2 multispectral images for: photosynthetically active vegetation (PV); and for combined non-photosynthetically active vegetation (NPV) and bare soil (BS).

The fCover product for the Netherlands has been generated by processing 10 cloud-free Sentinel-2 tiles which covered the country on 8 September 2016. The map has a spatial resolution of 60m x 60m. The Sentinel-2 scene classification layer was used to ensure that the spectral unmixing was only performed on areas of vegetation or soil.

The abundance maps were made by performing MESMA unmixing on each pixel from an endmember library of PV and combined NPV + BS spectra. The purest pixels in a scene, called endmembers, were extracted using the Spatial-Spectral Endmember Extraction (SSEE) approach. The PV and NPV+BS endmembers were classified with a random forest approach and selected to form the spectral library. The spectral library was used in the µMESMA unmixing to get the PV and NPV+BS abundances.

Luft- und Raumfahrt
Straßen
Bereitgestellt durch

German Aerospace Center (DLR)

Art des Datenzugangs

WWW / WMS

Aktualität der Datensatzbeschreibung

19.02.2021

Zeitbezug der Daten

08.09.2016

Aktualisierungsfrequenz

Niemals

Raumbezug

World Settlement Footprint (WSF) 2019 - Sentinel-1/2 - Global

The World Settlement Footprint (WSF) 2019 is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Based on the hypothesis that settlements generally show a more stable behavior with respect to most land-cover classes, temporal statistics are calculated for both S1- and S2-based indices. In particular, a comprehensive analysis has been performed by exploiting a number of reference building outlines to identify the most suitable set of temporal features (ultimately including 6 from S1 and 25 from S2). Training points for the settlement and non-settlement class are then generated by thresholding specific features, which varies depending on the 30 climate types of the well-established Köppen Geiger scheme. Next, binary classification based on Random Forest is applied and, finally, a dedicated post-processing is performed where ancillary datasets are employed to further reduce omission and commission errors. Here, the whole classification process has been entirely carried out within the Google Earth Engine platform. To assess the high accuracy and reliability of the WSF2019, two independent crowd-sourcing-based validation exercises have been carried out with the support of Google and Mapswipe, respectively, where overall 1M reference labels have been collected based photointerpretation of very high-resolution optical imagery.

Luft- und Raumfahrt
Straßen
Bereitgestellt durch

German Aerospace Center (DLR)

Art des Datenzugangs

WWW / WMS

Aktualität der Datensatzbeschreibung

20.01.2021

Zeitbezug der Daten

01.01.2019 — 31.12.2019

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

World Settlement Footprint (WSF) 2015 v2 - Landsat-8/Sentinel-1 - Global

The World Settlement Footprint WSF 2015 version 2 (WSF2015 v2) is a 10m resolution binary mask outlining the extent of human settlements globally for the year 2015. Specifically, the WSF2015 v2 is a pilot product generated by combining multiple datasets, namely:
• The WSF2015 v1 derived at 10m spatial resolution by means of 2014-2015 multitemporal Landsat-8 and Sentinel-1 imagery (of which ~217K and ~107K scenes have been processed, respectively); https://doi.org/10.1038/s41597-020-00580-5
• The High Resolution Settlement Layer (HRSL) generated by the Connectivity Lab team at Facebook through the employment of 2016 DigitalGlobe VHR satellite imagery and publicly released at 30m spatial resolution for 214 countries; https://arxiv.org/pdf/1712.05839.pdf
• The novel WSF2019 v1 derived at 10m spatial resolution by means of 2019 multitemporal Sentinel-1 and Sentinel-2 imagery (of which ~ 1.2M and ~1.8M scenes have been processed, respectively); https://doi.org/10.1553/giscience2021_01_s33
The WSF2015 v1 demonstrated to be highly accurate, outperforming all similar existing global layers; however, the use of Landsat imagery prevented a proper detection of very small structures, mostly due to their reduced scale. Based on an extensive qualitative assessment, wherever available the HRSL layer shows instead a systematic underestimation of larger settlements, whereas it proves particularly effective in identifying smaller clusters of buildings down to single houses, thanks to the employment of 2016 VHR imagery. The WSF2015v v2 has been then generated by: i) merging the WSF2015 v1 and HRSL (after resampling to 10m resolution and disregarding the population density information attached); and ii) masking the outcome by means of the WSF2019 product, which exhibits even higher detail and accuracy, also thanks to the use of Sentinel-2 data and the proper employment of state-of-the-art ancillary datasets (which allowed, for instance, to effectively mask out all roads globally from motorways to residential).

Luft- und Raumfahrt
Straßen
Bereitgestellt durch

German Aerospace Center (DLR)

Art des Datenzugangs

WWW / WMS

Aktualität der Datensatzbeschreibung

20.01.2021

Zeitbezug der Daten

01.01.2019 — 31.12.2019

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Multispectral Georeferenced Aerial Images of Northern Germany

General Information


Aerial images recorded on board of a research aircraft during flight trials in winter and summer of the year 2020. The images show the views of front-and down-facing cameras during en-route flights at altitudes from 2000 to 5000 ft AGL. Four cameras with different resolutions, frequencies and spectral measurement ranges were used.
The measurements include:
   • RGB images
   • IR images
   • Time stamps
   • Positions and attitudes of the cameras in WGS84

Flight data from following campaigns in February and June are included:

Date Cams Down Cams Ahead Route / Comment
2020-02-04 RGB IR Test flight. Down facing RGB without GNSS data.
2020-02-05 RGB IR RGB IR Uelzen - Schwerin - Salzwedel
2020-02-07 RGB IR RGB IR Hannover - Damme - Hameln
2020-02-21 RGB IR RGB WOB - Stendal - Klietz/Scharlibbe
2020-02-24 RGB IR Göttingen - Kassel - Höxter
2020-02-25 RGB IR Magdeburg - Leipzig - Naumburg
2020-03-02 RGB IR RGB IR Hildesheim - Paderborn


Date Cams Down Cams Ahead Route / Comment
2020-06-02 RGB IR RGB IR Photogrammetry flight over Braunschweig
2020-06-03 RGB IR RGB IR Rostock - Stralsund - Greifswald
2020-06-05 RGB IR RGB IR Greifswald - Peenemünde - Swinemünde
2020-06-08 RGB IR Dresden - Bautzen - Magdeburg
2020-06-09 RGB IR RGB IR Sangerhausen - Erfurt - Goslar
2020-02-04 RGB IR Test flight. Down facing RGB without GNSS data.

Password for data download: IFF-{Date of flight} - e.g. IFF-2020-02-05

Project: C2Land Phase C1 (FKZ 50 NA 1918) funded by the German Federal Ministry for Economic Affairs and Energy, administered by the Agency of Aeronautics of the DLR in Bonn.

Data Description


Flight trials were conducted with four cameras.
Downfacing: Phase One 100 MP RGB camera and Flir A65 IR camera
Ahead: PhotonFocus RGB camera and Infratec VarioCam 620 IR camera.

Pictures of cameras facing ahead are not available for all flights and flight segments.

For all pictures the current GPS/UTC time and position are available. For downfacing IR and both cameras facing ahead the euler angles of the aircraft are also available.

For each day (a single flight per day was performed) there is INS.kml to get an overview of the flown route.

Information about the data format


Phase One / Down facing RGB camera

The raw data of the camera was captured with 100MP resolution. The published data contains 1MP and 10MP downsampled data. Full resolution is available on request.
Pictures were taken in 3 second intervals.
The file name of the pictures contains their respective UTC time stamp.
The EXIF-tag of each picture contains the respective GPS coordinates as well as UTC time stamp.

PhotonFocus / RGB camera facing ahead

Pictures were taken at 10Hz with 1280x1024 pixel resolution.

Flir A65 / Down facing IR camera

Pictures were taken at 7.5Hz with 640x480 pixel resolution and upscaled to 1280x1024 pixel.
Camera native proprietary temparature mapping applied.

Infratec / IR camera facing ahead

Pictures were taken at 10Hz with 640x480 pixel resolution.
The provided jpeg images were created using min/max temparature mapping (i.e. black = coldest temperature in the image, white = hottest temperature in the image) and upscaled to 1280x1024 pixel.

In addition the raw data is available in a simple binary format containing float values for the temperature of each pixel.
The camera has a 640x480 pixel sensor capturing temperatures in the range from -40°C to +120°C with a temperature resolution of 0.03K at +30°C and a measuring accuracy of +/-1K.
The raw data files contain little endian 4-byte signed float values for the temperature in Kelvin (K) of each pixel sorted row-wise starting with the top-left pixel.

PhotonFocus, Flir, Infratec: State Vector

The pictures (and Infratec raw data files) follow the naming scheme frame_NNNNNN.jpg / frame_NNNNNN.irraw - with NNNNNN representing the frame ID.
Each record folder contains a saveData.csv file containing state vector information for each frame ID as well as GPS dayseconds as time information.

Luft- und Raumfahrt
Bereitgestellt durch

Technische Universität Braunschweig - Institut für Flugführung

Art des Datenzugangs

Dateidownload

Aktualität der Datensatzbeschreibung

20.07.2020

Zeitbezug der Daten

04.02.2020 — 09.06.2020

Aktualisierungsfrequenz

Niemals

Raumbezug

Land Cover DE - Sentinel-2 - Germany, 2015

This land cover classfication of Germany was created using Sentinel-2 imagery from the years 2015 to 2017 and LUCAS 2015 in-situ reference data (https://ec.europa.eu/eurostat/web/lucas). It contains seven land cover types: (1) artificial land, (2) open soil, (3) high seasonal vegetation, (4) high perennial vegetation, (5) low seasonal vegetation, (6) low perennial vegetation and (7) water with a spatial resolution of 10m x 10m. For further information, please see the following publication: https://doi.org/10.1016/j.jag.2020.102065

Luft- und Raumfahrt
Straßen
Bereitgestellt durch

German Aerospace Center (DLR)

Art des Datenzugangs

WWW / WMS

Aktualität der Datensatzbeschreibung

18.06.2020

Zeitbezug der Daten

27.06.2015 — 29.09.2017

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

Sentinel-2 MSI - Vegetation Indices (AGRO-DE) - Germany, Monthly Composites

This product comprises monthly composites and temporal statistics of selected vegetation indices (VI) for all of Germany from 2015 to today in 10m resolution, which were calculated using the DLR TimeScan processor. VIs (EVI, HA56, NDRE, NDVI, NDWI, PSRI and REIP) were calculated from Sentinel-2 Level 2A data at 10m spatial resolution produced by means of the DLR-PACO processor. Monthly compositing and temporal statistics are based on all valid observations per vegetation index. Derived variables per index are: minimum, maximum, mean and standard-deviation as well as the number of valid observations. Products are available in tiles according to the ESA Sentinel 2 granule grid (UTM). This is a product of AGRO-DE project (https://agro-de.info/).

Luft- und Raumfahrt
Straßen
Bereitgestellt durch

German Aerospace Center (DLR)

Art des Datenzugangs

WWW / WMS

Aktualität der Datensatzbeschreibung

18.06.2020

Zeitbezug der Daten

01.07.2015 —

Aktualisierungsfrequenz

Unregelmäßig

Raumbezug

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