本数据集基于Unet模型的Google Earth遥感影像“四乱”目标识别方法，针对黄河流域“四乱”问题，利用Google Earth遥感影像构建样本库，并利用Unet模型进行“四乱”目标的自动检测与识别，能够大大提高“四乱”问题目标的检测精度与效率，从而为黄河流域的生态环境保护和高质量发展提供有力支持。
", "ds_source": "实验数据集的图片来源于 Google Earth， 总共包含 932 张图片。这些图片分为四个类别，分别是乱占（141张）、乱建（332张）、乱堆（238张）和乱采（221张）[8, 9]。为了增加数据的多样性，对四种类别的图像进行数据增强，包括翻转、裁剪、旋转等，共获得9320张图片。
", "ds_process_way": "Unet模型是语义分割中较为优秀的模型之一，兼顾轻量化与高性能的优点。其结构是在Fully Convolutional Networks (FCN) 网络架构的基础上拓展而来，能够在训练样本较少的情况下发挥较好的分割效果，并能融合高维和低维特征，更适用于分割大幅图像。
", "ds_quality": "模型训练共采用了特征数据9320个。其中，7456个样本用于训练模型，1864个样本用于模型精度验证，预测精度为0.962。
", "ds_acq_start_time": "2023-01-01 00:00:00", "ds_acq_end_time": "2023-12-31 00:00:00", "ds_acq_place": "渭河流域", "ds_acq_lon_east": 110.27444444444444, "ds_acq_lat_south": 33.69611111111111, "ds_acq_lon_west": 103.9713888888889, "ds_acq_lat_north": 37.40833333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 155217411, "ds_files_count": 2, "ds_format": "jpg、txt", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "f7bc1344-d24b-4d12-baf1-bf0eea954bd1.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4520" ], "quality_level": 3, "publish_time": "2023-12-25 14:37:42", "last_updated": "2025-12-05 12:01:11", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB4127.2023", "i18n": { "en": { "title": "A Sample Dataset of \"Four Disorders\" in Rivers and Lakes Based on Unet Model", "ds_format": "jpg、txt", "ds_source": "The experimental dataset consists of 932 images sourced from Google Earth. These images are divided into four categories: disorderly occupation (141 images), disorderly construction (332 images), disorderly stacking (238 images), and disorderly collection (221 images) [8,9]. In order to increase data diversity, data augmentation was performed on four categories of images, including flipping, cropping, and rotation, resulting in a total of 9320 images.
", "ds_quality": "A total of 9320 feature data were used for model training. Among them, 7456 samples were used for training the model, 1864 samples were used for model accuracy verification, and the prediction accuracy was 0.962.
", "ds_ref_way": "", "ds_abstract": "This dataset is based on the Unet model's Google Earth remote sensing image \"four chaos\" target recognition method. In response to the \"four chaos\" problem in the Yellow River Basin, a sample library is constructed using Google Earth remote sensing images, and the Unet model is used for automatic detection and recognition of \"four chaos\" targets. This can greatly improve the detection accuracy and efficiency of \"four chaos\" problem targets, thereby providing strong support for the ecological environment protection and high-quality development of the Yellow River Basin.
", "ds_time_res": "", "ds_acq_place": "Weihe River Basin", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The Unet model is one of the excellent models in semantic segmentation, balancing the advantages of lightweight and high performance. Its structure is an extension of the Full Convolutional Networks (FCN) network architecture, which can perform well in segmentation with fewer training samples and integrate high-dimensional and low dimensional features, making it more suitable for segmenting large images.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "四乱", "黄河", "Unet", "检测", "深度学习" ], "ds_subject_tags": [ "人文地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄河流域" ], "ds_time_tags": [ 2023 ], "ds_contributors": [ { "true_name": "康建芳", "email": "kangjf@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "刘天山", "email": "851297938@qq.com", "work_for": "甘肃省水利信息中心", "country": "中国" }, { "true_name": "张保卫", "email": "zhangbaowei@zzu.edu.cn", "work_for": "郑州大学", "country": "中国" }, { "true_name": "何一飞", "email": "heyifei20@mails.ucas.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "张耀南", "email": "yaonan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "康建芳", "email": "kangjf@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "刘天山", "email": "851297938@qq.com", "work_for": "甘肃省水利信息中心", "country": "中国" }, { "true_name": "张保卫", "email": "zhangbaowei@zzu.edu.cn", "work_for": "郑州大学", "country": "中国" }, { "true_name": "何一飞", "email": "heyifei20@mails.ucas.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "张耀南", "email": "yaonan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "康建芳", "email": "kangjf@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "社会经济文化" }