基于时空卷积网络的农用地块划定
Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks
摘要：
农场地块划定提供了地籍数据，对制定和管理气候变化政策至关重要。具体而言，农场地块划定告知土地分配，灌溉，施肥，温室气体（GHG's）等下游政府政策的应用程序。这些数据也可以用于农业保险部门评估与极端天气事件相关的损害赔偿-这是一个与气候变化相关的增长趋势。使用卫星成像可以是一种可扩展的和具有成本效益的方式来执行农场地块划定任务，以收集这一宝贵的数据。在本文中，我们打破了这个任务，使用卫星成像分为两种方法：1）分割的地块边界，2）分割的地块面积。我们实施了UNets的变体，其中一个考虑了时间信息，这在我们2017年法国农田地块数据集上取得了最好的结果。
英文摘要：
Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. Specifically, farm parcel delineation informs applications in downstream governmental policies of land allocation, irrigation, fertilization, green-house gases (GHG's), etc. This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events - a growing trend related to climate change. Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation to collect this valuable data. In this paper, we break down this task using satellite imaging into two approaches: 1) Segmentation of parcel boundaries, and 2) Segmentation of parcel areas. We implemented variations of UNets, one of which takes into account temporal information, which achieved the best results on our dataset on farmland parcels in France in 2017.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
