论文标题
使用时空卷积网络的农田包裹划定
Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks
论文作者
论文摘要
Farm Parcel描述提供了心脏数据，这些数据对于制定和管理气候变化政策很重要。具体而言，农场包裹划定为土地分配，灌溉，施肥，绿色房屋气体（GHG）等政府政策的应用提供了信息。该数据也可能对与极端天气事件相关的损害损害的农业保险部门也很有用，这与极端天气事件相关 - 与气候变化相关。使用卫星成像可以是一种可扩展且具有成本效益的方式，以执行农业包裹描绘的任务以收集这些有价值的数据。在本文中，我们使用卫星成像分解为两种方法：1）包裹边界的分割，以及2）包裹区域的分割。我们实施了各种UNET的变化，其中之一考虑到时间信息，这在2017年在法国的Farmland包裹上取得了最佳结果。
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.
