基于深度学习的建筑物识别及占用耕地建房自动化监测——以湖南省长沙市X村为例

doi:10.14088/j.cnki.issn0439-8114.2024.01.035

湖北农业科学 ›› 2024, Vol. 63 ›› Issue (1): 195-198.doi: 10.14088/j.cnki.issn0439-8114.2024.01.035

基于深度学习的建筑物识别及占用耕地建房自动化监测——以湖南省长沙市X村为例

石珊¹, 胡兵¹, 杨丛瑞²

1.长沙市规划信息服务中心,长沙 410006;
2.红河州自然资源和规划局,云南红河州 661100

收稿日期:2022-09-13 出版日期:2024-01-25 发布日期:2024-02-05
作者简介:石珊（1996-）,女,湖南长沙人,硕士,主要从事遥感影像处理及规划创新研究,（电话）15674803216（电子信箱）shishan1106@163.com。
基金资助:
国土资源评价与利用湖南省重点实验室开放课题项目（SYS-ZX-202005）

Building recognition and automated monitoring of occupying farmland for construction based on deep learning：Taking X Village in Changsha City, Hunan Province as an example

SHI Shan¹, HU Bing¹, YANG Cong-rui²

1. Changsha Urban Planning Information Service Center, Changsha 410006, China;
2. Natural Resources and Planning Bureau of Honghe, Honghe Prefecture 661100, Yunnan, China

Received:2022-09-13 Online:2024-01-25 Published:2024-02-05

摘要/Abstract

摘要： 针对农村普遍存在的占用耕地建房现象,基于深度学习和图像分析提出了一种自动化监测方法,通过对高分辨率遥感影像数据的预处理,构建基于卷积神经网络的自动化监测模型,有效判定目标影像中的每个像元格是否占用耕地建房。以湖南省长沙市X村为例,横向比较U-Net、SegNet、DeepLabV3p模型的识别能力。结果表明,当学习率为0.01、批大小为2、迭代次数为100次时,U-Net模型对建筑物的识别结果最佳;该模型共发现66宗潜在占用耕地建房案例,识别结果准确率高且耗时短;该模型充分运用了现代信息技术及方法,可在一定程度提高土地执法监察的工作效率、节省工作时间及资源。

关键词: 深度学习, U-Net模型, 自动化监测, 建筑物识别, 占用耕地, 土地执法, 湖南省长沙市

Abstract: In response to the common phenomenon of occupying farmland for building houses in rural areas, an automated monitoring method based on deep learning and image analysis was proposed. By preprocessing high-resolution remote sensing image data, an automated monitoring model based on convolutional neural networks was constructed to effectively determine whether each pixel cell in the target image occupied farmland for building houses. Taking X Village in Changsha City, Hunan Province as an example, horizontally compared the recognition capabilities of U-Net, SegNet, and DeepLabV3p models were. The results showed that when the learning rate was 0.01, the batch size was 2, and the number of iterations was 100, the U-Net model had the best recognition results for buildings;the model found a total of 66 cases of potential occupation of farmland for building houses, with high recognition accuracy and less time consumption;this model fully utilized modern information technology and methods, which could improve the efficiency of land law enforcement and supervision to a certain extent and save work time and resources.

Key words: deep learning, U-Net model, automated monitoring, building recognition, occupation of farmland, land enforcement, Changsha City,Hunan Province

中图分类号:

P237

石珊, 胡兵, 杨丛瑞. 基于深度学习的建筑物识别及占用耕地建房自动化监测——以湖南省长沙市X村为例[J]. 湖北农业科学, 2024, 63(1): 195-198.

SHI Shan, HU Bing, YANG Cong-rui. Building recognition and automated monitoring of occupying farmland for construction based on deep learning：Taking X Village in Changsha City, Hunan Province as an example[J]. HUBEI AGRICULTURAL SCIENCES, 2024, 63(1): 195-198.

参考文献

[1] 牛善栋,方斌.中国耕地保护制度70年:历史嬗变、现实探源及路径优化[J].中国土地科学, 2019, 33(10): 1-12.
[2] 卢绍婷,周俊晖.农村乱占耕地建房整治方法研究与系统实现[J].国土资源信息化, 2021(2): 48-53.
[3] LI M,STEIN A,KMD B.A bayesian characterization of urban land use configurations from VHR remote sensing images[J]. International journal of applied earth observation and geoinformation, 2020, 10:102175.
[4] 陈琳,刘清华.城镇和农村住宅用地使用现状及整治建议——以湖北省钟祥市为例[J]. 中国土地, 2020(5): 52-53.
[5] 吴家杰,黄霞,刘锟铭.农村乱占耕地建房疑似违法图斑自动提取研究[J].江西测绘, 2020(4):37-38, 41.
[6] 夏晓鸿,潘俊国,董海潮,等.全省农村土地综合监管机制创新调研报告[J].浙江国土资源, 2018(11): 35-37.
[7] 周建国. 农村耕地非法转为宅基地的问题与对策[J].贵州农业科学, 2010, 38(2): 183-188.
[8] 王丹,张璐,柴燕妮.基于深度学习的乱占耕地建房疑似图斑自动提取方法研究[J].测绘与空间地理信息,2022,45(6): 51-53,57.
[9] YU H, YANG Z, TAN L, et al.Methods and datasets on semantic segmentation: A review[J]. Neurocomputing, 2018, 304, 82-103.
[10] CROMMELINCK S,KOEVA M,YANG M Y,et al.Application of deep learning for delineation of visible cadastral boundaries from remote sensing imagery[J]. Remote sensing, 2019,11(21): 2505.
[11] LECUN Y, BENGIO Y, HINTON G.Deep learning[J]. Nature, 2015, 521:436-444.
[12] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[J]. IEEE transactions on pattern analysis and machine intelligence, 2015,39(4): 640-651.
[13] PERSELLO C, STEIN A.Deep fully convolutional networks for the detection of informal settlements in VHR images[J]. IEEE geoscience and remote sensing letters, 2017,14(12): 2325-2329.
[14] 范荣双,陈洋,徐启恒,等.基于深度学习的高分辨率遥感影像建筑物提取方法[J].测绘学报, 2019, 48(1):34-41.
[15] 季顺平,田思琦,张驰.利用全空洞卷积神经元网络进行城市土地覆盖分类与变化检测[J].武汉大学学报(信息科学版), 2020, 45(2):233-241.
[16] 王舒洋,慕晓冬,杨东方,等.融合高阶信息的遥感影像建筑物自动提取[J].光学精密工程, 2019, 27(11):2474-2483.
[17] LIU R, KUFFER M, PERSELLO C.The temporal dynamics of slums employing a CNN-based change detection approach[J]. Remote sensing, 2019, 11(23):2844.
[18] XIA X, PERSELLO C, KOEVA M.Deep fully convolutional networks for cadastral boundary detection from UAV images[J]. Remote sensing, 2019, 11(14):1725.

基于深度学习的建筑物识别及占用耕地建房自动化监测——以湖南省长沙市X村为例

Building recognition and automated monitoring of occupying farmland for construction based on deep learning：Taking X Village in Changsha City, Hunan Province as an example

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价

[1]	袁鹏, 王珂, 肖坚. 基于RMAU-Net网络模型的高分影像耕地提取[J]. 湖北农业科学, 2023, 62(8): 182-188.
[2]	李正, 李宝喜, 李志豪, 战艺芳, 王利华, 龚琦. 基于深度学习的农作物病虫害识别研究进展[J]. 湖北农业科学, 2023, 62(11): 165-169.
[3]	邱儒琼, 彭少坤, 李孟璠. 基于深度学习和遥感数据的水稻种植面积提取[J]. 湖北农业科学, 2023, 62(11): 176-182.
[4]	张之凤, 唐玉萍, 潘洪义, 马红菊. 城镇扩张占用耕地时空格局及利用效率研究——以成都都市圈为例[J]. 湖北农业科学, 2023, 62(1): 47-53.
[5]	雷朦, 余顺园. 基于卷积神经网络的魔芋病害识别[J]. 湖北农业科学, 2022, 61(23): 197-201.
[6]	孙吉红, 钱晔, 周正, 张剑波. 深度学习环境下香料种植推荐模型的设计研究[J]. 湖北农业科学, 2022, 61(22): 174-177.
[7]	王培福, 孙一丹, 鹿子涵, 王伟, 陈晓峰. 基于深度学习的板栗分级方法研究[J]. 湖北农业科学, 2022, 61(21): 168-175.
[8]	陈行, 夏丽华, 颜军, 蒋晓旭, 黄腾杰, 邓剑文. 基于改进U-Net网络模型的高分影像养殖池塘提取[J]. 湖北农业科学, 2022, 61(20): 166-171.
[9]	罗宇, 罗林艳, 范嘉智, 段思汝, 高文娟. 基于深度GRU神经网络的逐小时气温预报模型[J]. 湖北农业科学, 2021, 60(6): 119-122.