Functional Map of the World

被引：175

作者：

Christie, Gordon ^{[1
]}

Fendley, Neil ^{[1
]}

Wilson, James ^{[2
]}

Mukherjee, Ryan ^{[1
]}

机构：

[1] Johns Hopkins Univ, Appl Phys Lab, Baltimore, MD 21218 USA

[2] DigitalGlobe, Westminster, CO USA

来源：

2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) | 2018年

关键词：

D O I：

10.1109/CVPR.2018.00646

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a new dataset, Functional Map of the World (fMoW), which aims to inspire the development of machine learning models capable of predicting the functional purpose of buildings and land use from temporal sequences of satellite images and a rich set of metadata features. The metadata provided with each image enables reasoning about location, time, sun angles, physical sizes, and other features when making predictions about objects in the image. Our dataset consists of over 1 million images from over 200 countries1. For each image, we provide at least one bounding box annotation containing one of 63 categories, including a "false detection" category. We present an analysis of the dataset along with baseline approaches that reason about metadata and temporal views. Our data, code, and pretrained models have been made publicly available.

引用

页码：6172 / 6180

页数：9