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The task_categories "change-detection" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
LEVIR CD+
The LEVIR-CD+ dataset is an urban building change detection dataset that focuses on RGB image pairs extracted from Google Earth. This dataset consists of a total of 985 image pairs, each with a resolution of 1024x1024 pixels and a spatial resolution of 0.5 meters per pixel. The dataset includes building and land use change masks for 20 different regions in Texas, spanning the years 2002 to 2020, with a time span of 5 years between observations. LEVIR-CD+ is designed as the easier version of the S2Looking dataset, primarily due to its urban locations and near-nadir angles.
Description
The bitemporal images in LEVIR-CD are from 20 different regions that sit in several cities in Texas of the US, including Austin, Lakeway, Bee Cave, Buda, Kyle, Manor, Pflugervilletx, Dripping Springs, etc. The Figure below illustrates the geospatial distribution of our new dataset and an enlarged image patch. The captured time of our image data varies from 2002 to 2018. Images in different regions may be taken at different times. We want to introduce variations due to seasonal changes and illumination changes into our new dataset, which could help develop effective methods that can mitigate the impact of irrelevant changes on real changes.
- Total Number of Images: 985
- Bands: 3 (RGB)
- Image Size: 1024x1024
- Image Resolution: 0.5m
- Land Cover Classes: 2
- Classes: no-change, change
- Source: Google Earth
Usage
To use this dataset, simply use datasets.load_dataset("blanchon/LEVIR_CDPlus").
from datasets import load_dataset
LEVIR_CDPlus = load_dataset("blanchon/LEVIR_CDPlus")
Citation
If you use the EuroSAT dataset in your research, please consider citing the following publication:
@article{Chen2020,
AUTHOR = {Chen, Hao and Shi, Zhenwei},
TITLE = {A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection},
JOURNAL = {Remote Sensing},
VOLUME = {12},
YEAR = {2020},
NUMBER = {10},
ARTICLE-NUMBER = {1662},
URL = {https://www.mdpi.com/2072-4292/12/10/1662},
ISSN = {2072-4292},
DOI = {10.3390/rs12101662}
}
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