Paper Info
Title
Robocodes: Towards Generative Street Addresses from Satellite Imagery.
Abstract
We describe our automatic generative algorithm to create street addresses (Robocodes) from satellite images by learning and labeling regions, roads, and blocks. 75% of the world lacks street addresses [12]. According to the United Nations, this means 4 billion people are 'invisible'. Recent initiatives tend to name unknown areas by geocoding, which uses latitude and longitude information. Nevertheless settlements abut roads and such addressing schemes are not coherent with the road topology. Instead, our algorithm starts with extracting roads and junctions from satellite imagery utilizing deep learning. Then, it uniquely labels the regions, roads, and houses using some graph- and proximity-based algorithms. We present our results on both cities in mapped areas and in developing countries. We also compare productivity based on current ad-hoc and new complete addresses. We conclude with contrasting our generative addresses to current industrial and open solutions.
Year
DOI
Venue
2017
10.1109/CVPRW.2017.192
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Field
DocType
Volume
Data science,Computer vision,Satellite,Satellite imagery,Geocoding,Computer science,Geographic coordinate system,Image segmentation,Artificial intelligence,Human settlement,Generative grammar,Deep learning
Conference
2017
Issue
ISSN
Citations 
1
2160-7508
2
PageRank 
References 
Authors
0.64
16
12
Name
Order
Citations
PageRank
Ilke Demir1466.00
Forest Hughes2252.45
Aman Raj361.10
Kleovoulos Tsourides420.64
Divyaa Ravichandran520.64
Suryanarayana Murthy620.64
Kaunil Dhruv761.44
Sanyam Garg861.44
Jatin Malhotra921.32
Barrett Doo1020.64
Grace Kermani1120.64
Ramesh Raskar125305422.69