A Low Altitude Geo-Referenced Drone Dataset
Post · Apr 6, 2017 15:02 ·
Change detection datasets can be hard to come by. Especially so with low altitude geolocated drone datasets. While there’s a lot of opportunity for mis-use of research and we’re uncomfortable with all the scenarios presented, there’s also a lot of humanitarian use cases as well. Drone delivery of medication to natural disaster areas is just one that comes to mind.
This paper announces the release of a new dataset of aerial video sequences to spur the development of mosaicking and change detection techniques.
Highlight From the Paper
- “The dataset is composed of two main sets of challenging video sequences acquired at very low-altitude.”
- ”(Set One) consists of 30 not geo-referenced sequences that can be used to evaluate mosaicking algorithms.”
- ”(Set Two) is made up of 10 pairs of geo-referenced sequences (i.e., 20 videos) in which the first can be used to build the mosaic and the second, acquired on the same path, can be used to test change detection algorithms.”
- Danilo Avola
- Gian Luca Foresti
- Niki Martinel
- Daniele Pannone
- Claudio Piciarelli
In recent years, the technological improvements of low-cost small-scale Unmanned Aerial Vehicles (UAVs) are promoting an ever-increasing use of them in different tasks. In particular, the use of small-scale UAVs is useful in all these low-altitude tasks in which common UAVs cannot be adopted, such as recurrent comprehensive view of wide environments, frequent monitoring of military areas, real-time classification of static and moving entities (e.g., people, cars, etc.). These tasks can be supported by mosaicking and change detection algorithms achieved at low-altitude. Currently, public datasets for testing these algorithms are not available. This paper presents the UMCD dataset, the first collection of geo-referenced video sequences acquired at low-altitude for mosaicking and change detection purposes. Five reference scenarios are also reported.
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