Remote Sensing
This dataset comprises UAV-acquired RGB image samples covering three distinct forest ecosystem types across multiple phenological seasons. Each dataset package contains high-resolution PNG-format aerial imagery paired with corresponding annotation files, maintaining consistent filenames between images and their pixel-level vegetation labels for seamless data association. The time-series acquisition strategy captures seasonal variations in canopy structure, coloration, and density within identical geographic coordinates.
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Wetland ecosystems are affected by climate change, tidal fluctuations, and human activities, causing significant spectral variations across different times and locations. These variations challenge hyperspectral classification models, limiting their generalization in cross-domain scenarios. However, most existing hyperspectral datasets focus on a single time point or region, lacking standardized resources for cross-temporal and cross-scene studies, which restricts the application of unsupervised domain adaptation (UDA) methods in wetland remote sensing.
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Monitoring bird populations is crucial to conserve biodiversity and protect wetland ecosystems. Unmanned aerial vehicle (UAV) remote sensing is characterized by high image resolution, strong maneuverability, and convenient data acquisition, and is thus well suited for monitoring bird species in wetland environments. However, strong light reflection from the water surface interferes with the accurate identification of birds on UAV remote sensing images.
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Monitoring bird populations is crucial to conserve biodiversity and protect wetland ecosystems. Unmanned aerial vehicle (UAV) remote sensing is characterized by high image resolution, strong maneuverability, and convenient data acquisition, and is thus well suited for monitoring bird species in wetland environments. However, strong light reflection from the water surface interferes with the accurate identification of birds on UAV remote sensing images.
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With the gradual maturity of UAV technology, it can provide extremely powerful support for smart agriculture and precise monitoring. Currently, there is no dataset related to green walnuts in the field of agricultural computer vision. Therefore, in order to promote the algorithm design in the field of agricultural computer vision, we used UAV to collect remote sensing data from 8 walnut sample plots.
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118 Views
These are three sets of simulated echo data generated by Matlab. The first set of echo data comes from a surface target with a size comparable to a 2-D MIMO array, but with a center point offset from the array. The second group contains three echo data from point targets. The first packet is a point target within the physical size of the array, the second packet is a point target outside the physical size of the array, and the third packet is the sum of the first two packets.
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24 Views
The aims of the experiment described were to analyse the effect of material differences and the highly curved surface of small cylinders on laser scanning data uncertainty, and to link the experimental results to theory.
To this end, seven cylinders with different surface properties were measured with a terrestrial laser scanner. The cylinders were chosen to represent materials common in the built and natural environments, in addition to having distinct reflectivity characteristics.
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122 Views
The AMD3IR dataset is a large-scale collection of Shortwave Infrared (SWIR) and Longwave Infrared (LWIR) images, designed to advance the ongoing research in the field of drone detection and tracking. It efficiently addresses key challenges such as detecting and distinguishing small airborne objects, differentiating drones from background clutter, and overcoming visibility limitations present in conventional imaging. The dataset comprises 20,865 SWIR images with 24,994 annotated drones and 8,696 LWIR images with 10,400 annotated drones, featuring various UAV models.
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257 Views
Known as the “Water Tower of Asia,” the Tibetan Plateau plays an important role not only in regional hydrology, but also in the larger, more global water cycle, as well as climate regulation. While this region plays such an important role, things get difficult when it comes to extracting water data using remote sensing techniques—primarily because of snow interference, which leads to all kinds of misjudgments and detection errors.
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242 Views