NJLCC2022 Dataset

Introduction to NJLCC2022 Dataset
dataset
Author

Bowen

Published

October 18, 2023

**Weipeng Shi, Wenhu Qin*, Zhonghua Yun, Chao Wu, Yukun Yang, and Tao Zhao**

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Abstract

The existing land cover classification dataset are collected under clear weather conditions. In practical applications, adverse climate conditions can significantly impact the model’s classification performance. However, the existing datasets, while suitable for model training and accuracy testing, are inadequate for evaluating classification robustness under adverse weather conditions like haze and fog. Consequently, when models trained on clean remote sensing images perform well on test datasets, the frequent occurrence of fog in real-world scenarios can severely interfere with the quality of remote sensing images, leading to reduced model classification accuracy and poor generalization. Therefore, this study collected remote sensing data in Nanjing under both sunny and foggy conditions using drones and named the dataset NJLCC2022 (NanJing Land Cover Classification in 2022). This approach helps: - better assess the degradation in model classification performance under foggy conditions compared to clean conditions. - investigate the commonalities in visual representations in remote sensing images under foggy conditions, thereby designing models with strong robustness. - use real remote sensing data from both clear and foggy conditions to enhance model transfer learning.

The dataset is from Shi W, Qin W, Yun Z, Wu C, Yang Y, Zhao T. Semantic Representation Fusion-Based Network for Robust Land Cover Classification in Foggy Conditions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-16.

Dataset

Description

This dataset was collected using the DJI Mavic 3 drone for remote sensing imagery. The optical imagery primarily includes three spectral bands: red, green, and blue. The drone flew at an altitude of 200 meters, with a ground sampling distance of 4 cm/pixel. As of April 1, 2023, this dataset contains a total of 22 orthorectified images. Among these, 9 images were affected by fog, while the remaining 13 were acquired under clear sky conditions. The average resolution of the images is 9000×9000 pixels. In order to reduce model bias and to compare land cover differences between urban and rural areas, the sampling locations of the 22 images are scattered between the urban area (Xuanwu District) and rural area (Jiangning District) of Nanjing, Jiangsu Province. The NJLCC2022 dataset includes annotations for 5 classes of objects, namely Clutter Background, Car, Playground, Water, and Building. Each class is annotated with a different color: Clutter Background (RGB: 0, 0, 0), Car (RGB: 0, 128, 0), Playground (RGB: 128, 0, 128), Water (RGB: 0, 0, 128), and Building (RGB: 128, 0, 0). ## Details ### Locations and Data Collection Dates

Sample ID Collection Date Fog Type Longitude Latitude
ortho1 2023-02-06 Thick 118.818251 32.064312
ortho2 2023-02-06 Thick 118.823017 32.065051
ortho3 2023-02-06 Thick 118.816626 32.066614
ortho4 2022-11-03 Clear 118.809120 32.054298
ortho5 2022-12-07 Moderate 118.819967 32.072009
ortho6 2022-12-09 Thin 118.825302 32.072846
ortho7 2022-12-07 Moderate 118.861529 31.914914
ortho8 2022-12-09 Thin 118.869680 31.925127
ortho9 2022-12-09 Thin 118.870319 31.915301
ortho10 2022-12-07 Moderate 118.874873 31.914486
ortho11 2022-10-11 Clear 118.801179 32.069022
ortho12 2022-12-13 Clear 118.805841 32.058883
ortho15 2022-12-15 Clear 118.793864 32.060306
ortho16 2022-12-15 Clear 118.783131 32.068561
ortho17 2022-12-19 Clear 118.779321 32.062508
ortho18 2022-12-22 Clear 118.784110 32.066938
ortho19 2022-12-17 Clear 118.795185 32.056631
ortho20 2022-12-17 Clear 118.798349 32.058409
ortho21 2022-12-22 Clear 118.799249 32.054417
ortho22 2023-01-06 Clear 118.788154 32.058070
ortho25 2022-10-18 Clear 118.795385 32.063230
ortho26 2022-10-19 Clear 118.813055 32.057664

Location Map

# Images ## Annotation ## Samples

Download

If you want to use dataset for model training and evaluation, please send an email to bowenroom1@gmail.com for a request and clarify the purpose.

Citation

@article{shiSemanticRepresentationFusionBased2023,
  title = {Semantic {{Representation Fusion-Based Network}} for {{Robust Land Cover Classification}} in {{Foggy Conditions}}},
  author = {Shi, Weipeng and Qin, Wenhu and Yun, Zhonghua and Wu, Chao and Yang, Yukun and Zhao, Tao},
  year = {2023},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  volume = {61},
  pages = {1--16},
  issn = {1558-0644},
  doi = {10.1109/TGRS.2023.3280158}
}

Contact

E-mail: qinwenhu@seu.edu.cn,bowenroom1@gmail.com