Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks

• URL: http://arxiv.org/abs/2004.05471v2
• Date: Mon, 20 Apr 2020 16:34:41 GMT
• Title: Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks
• Authors: Han Lin Aung, Burak Uzkent, Marshall Burke, David Lobell, Stefano Ermon
• Abstract summary: Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events. Using satellite imaging can be a scalable and cost effective manner to perform the task of farm parcel delineation.
• Score: 77.63950365605845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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