Geo-Sensing
\textit{1) Hyrank dataset:}
The dataset was developed with the support of the ISPRS Scientific Committee, taken by the Hyerion sensor carried by NASA's EO-1 satellite. After screening 242 spectral bands from SWIR and VNIR sensors, 176 spectral bands were exported. The total of 5 images and 14 land-cover categories were provided. The Dioni image size is $250 \times1376 $, and the Loukia image size is $249 \times945 $, both with a spatial resolution of 30m. Two scene images are shown in Fig. \ref{Hyrank}.
\textit{2) Houston dataset:}
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FLAME2-DT (Forest Fire Detection Dataset with Dual-modality Labels) is a comprehensive multi-modal dataset specifically designed for UAV-based forest fire detection research. The dataset consists of 1,280 paired RGB-thermal infrared images captured by a Mavic 2 Enterprise Advanced UAV system, with high-resolution (640×512) and precise pixel-level annotations for both fire and smoke regions. This dataset addresses critical challenges in forest fire detection by providing paired multi-modal data that captures the complementary characteristics of visible light and thermal imaging.
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The dataset used in this study combines remote sensing data from multiple advanced platforms, including Synthetic Aperture Radar (SAR) from Sentinel-1, multispectral imagery from Sentinel-2, and LiDAR measurements from the Global Ecosystem Dynamics Investigation (GEDI) mission. Each of these sources offers unique and complementary information, enabling a detailed and comprehensive analysis of forest canopy height across diverse and ecologically significant regions in northern Vietnam.
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Decentralized Collaborative Simultaneous Localization and Mapping (C-SLAM) is essential to enable multi-robot missions in unknown environments without relying on pre-existing localization and communication infrastructure. This technology is anticipated to play a key role in the exploration of the Moon, Mars, and other planets. In this work, we introduce a novel dataset collected during C-SLAM experiments involving three robots operating on a Mars analogue terrain.
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Synthetic Aperture Radar (SAR) imagery plays a vital role in identifying flooded areas in the aftermath causing loss of life and significant economic and environmental damage, as water surfaces reflect less microwave energy compared to land due to their smooth texture and low surface roughness.
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This dataset is from "One-Stage Cascade Refinement Networks for Infrared Small Target Detection." It includes 427 infrared images and 480 targets (due to the lack of infrared sequences, SIRST also contains infrared images at a wavelength of 950 nm, in addition to shortwave and midwave infrared images). Approximately 90% of the images contain only one target, while about 10% have multiple targets (which may be overlooked in sparse/significant methods due to global unique assumptions).
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Experiment Details:
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This dataset was obtained from the ionogram at Kupang in 2022 to investigate the frequency window of the Near Vertical Incidence Skywave (NVIS) channel with homogenous Time of Flight (ToF) values. All data is in the form of.mat files (MATLAB). The first dataset is in Matlab variables ('DataWidthFrekHourly','DataCenterWidthFrekHourly'), which consist of maximum window frequency values with homogenous ToF and the center frequency of the widest window. The structure of the data array is 96x365, with the row as the time (1:96) and the column as the day in one year (1:365).
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We employed Hexacopter unmanned aerial vehicle (UAV) equipped with the SPECIM FX17E hyperspectral camera to implement ultra-low-altitude flight aerial photography missions with atmospheric correction processing. We collected three hyperspectral images and combined them into three data pairs, which exhibit varying degrees of spectral shift. Among them, a hyperspectral image including six types of ground objects was collected in Changsha at 4:00 pm on September 27, 2021, with sunny weather and a flight altitude of 30m, named CSSunny.
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