{"type":"ScholarlyArticle","title":"Automatic Grassland Degradation Estimation Using Deep Learning","authors":[{"name":"Xiyu Yan","slug":"xiyu-yan","given":"Xiyu","family":"Yan","affiliation":"Dept. of Computer Science and Technology , Tsinghua University"},{"name":"Yong Jiang","slug":"yong-jiang","given":"Yong","family":"Jiang","affiliation":"Dept. of Computer Science and Technology , Tsinghua University; PCL Research Center of Networks and Communications , Peng Cheng Laboratory 3 Baidu , Inc ."},{"name":"Shuai Chen","slug":"shuai-chen","given":"Shuai","family":"Chen","affiliation":""},{"name":"Zihao He","slug":"zihao-he","given":"Zihao","family":"He","affiliation":"Dept. of Computer Science and Technology , Tsinghua University"},{"name":"Chunmei Li","slug":"chunmei-li","given":"Chunmei","family":"Li","affiliation":"Dept. of Computer Technology and Applications , Qinghai University"},{"name":"Shu-Tao Xia","slug":"shu-tao-xia","given":"Shu-Tao","family":"Xia","affiliation":"Dept. of Computer Science and Technology , Tsinghua University; PCL Research Center of Networks and Communications , Peng Cheng Laboratory 3 Baidu , Inc ."},{"name":"Tao Dai","slug":"tao-dai","given":"Tao","family":"Dai","affiliation":"Dept. of Computer Science and Technology , Tsinghua University; PCL Research Center of Networks and Communications , Peng Cheng Laboratory 3 Baidu , Inc ."},{"name":"Shuo Dong","slug":"shuo-dong","given":"Shuo","family":"Dong","affiliation":"Dept. of Computer Technology and Applications , Qinghai University"},{"name":"Feng Zheng","slug":"feng-zheng","given":"Feng","family":"Zheng","affiliation":"Dept. of Computer Science and Engineering , Southern University of Science and Technology"}],"abstract":"Grassland degradation estimation is essential to prevent global land desertification and sandstorms. Typically, the key to such estimation is to measure the coverage of indicator plants. However, traditional methods of estimation rely heavily on human eyes and manual labor, thus inevitably leading to subjective results and high labor costs. In contrast, deep learning-based image segmentation algorithms are potentially capable of automatic assessment of the coverage of indicator plants. Nevertheless, a suitable image dataset comprising grassland images is not publicly available. To this end, we build an original Automatic Grassland Degradation Estimation Dataset (AGDE-Dataset), with a large number of grassland images captured from the wild. Based on AGDE-Dataset, we are able to propose a brand new scheme to automatically estimate grassland degradation, which mainly consists of two components. 1) Semantic segmentation: we design a deep neural network with an improved encoder-decoder structure to implement semantic segmentation of grassland images. In addition, we propose a novel Focal-Hinge Loss to alleviate the class imbalance of semantics in the training stage.  2) Degradation estimation: we provide the estimation of grassland degradation based on the results of semantic segmentation. Experimental results show that the proposed method achieves satisfactory accuracy in grassland degradation estimation.","doi":"10.24963/ijcai.2019/835","url":"https://ijcai.org/proceedings/2019/835","pdf":"https://ijcai.org/proceedings/2019/0835.pdf","markdown":"https://ijcai.org/proceedings/2019/835.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/835","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"AI for Improving Human Well-being","track":"AI for Improving Human Well-being","number":835,"pages":{"first":6028,"last":6034},"keywords":["Special Track on AI for Improving Human-Well Being: AI applications for Improving Human-Well Being (Special Track on AI and Human Wellbeing)"],"videos":[],"references":[{"position":1,"raw":"[Badrinarayanan et al., 2017] Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 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