Image Processing
This aerial image dataset consists of more than 22,000 independent buildings extracted from aerial images with 0.0075 m spatial resolution and 450 km^2 covering in Christchurch, New Zealand. The most parts of aerial images are down-sampled to 0.3 m ground resolution and cropped into 8,189 non-overlapping tiles with 512* 512. These tiles make up the whole dataset. They are split into three parts: 4,736 tiles for training, 1,036 tiles for validation and 2,416 tiles for testing.
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This Dataset contains "Pristine" and "Distorted" videos recorded in different places. The
distortions with which the videos were recorded are: "Focus", "Exposure" and "Focus + Exposure".
Those three with low (1), medium (2) and high (3) levels, forming a total of 10 conditions
(including Pristine videos). In addition, distorted videos were exported in three different
qualities according to the H.264 compression format used in the DIGIFORT software, which were:
High Quality (HQ, H.264 at 100%), Medium Quality (MQ, H.264 at 75%) and Low Quality
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This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training.
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Dataset asscociated with a paper in IEEE Transactions on Pattern Analysis and Machine Intelligence
"The perils and pitfalls of block design for EEG classification experiments"
DOI: 10.1109/TPAMI.2020.2973153
If you use this code or data, please cite the above paper.
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Extracting the boundaries of Photovoltaic (PV) plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, Operation and Maintenance (O&M) service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants.
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Accurate information about crop rotation is essential for administrators, managers and various government departments for assessment, monitoring, and management of various resources for crop escalation. Radar remote sensing, because of its all-weather capability and assured uninterrupted data supply can show a substantial part in the evaluation of crop rotation.
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A custom made multispectral camera was used to collect a novel dataset of images of untreated lettuce leaves or leaves treated with vinegar, oil, or a combination of these. The camera captured image data at 10 wavelengths ∈[380nm,980nm] across the electromagnetic spectrum in the visible and NIR (near-infrared) regions. Imaging was done in a lab environment with the presence of ambient light.
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This dataset is a collection of images and their respective labels containing examples of multiple Brazilian coins, the primary purpose is to support the development of Computer Vision techniques for automatic detection of such objects, i.e., localization and classification tasks.
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