TY - GEN
T1 - A weakly supervised approach for estimating spatial density functions from high-resolution satellite imagery
AU - Jacobs, Nathan
AU - Kraft, Adam
AU - Rafique, Muhammad Usman
AU - Sharma, Ranti Dev
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/11/6
Y1 - 2018/11/6
N2 - We propose a neural network component, the regional aggregation layer, that makes it possible to train a pixel-level density estimator using only coarse-grained density aggregates, which reflect the number of objects in an image region. Our approach is simple to use and does not require domain-specific assumptions about the nature of the density function. We evaluate our approach on several synthetic datasets. In addition, we use this approach to learn to estimate high-resolution population and housing density from satellite imagery. In all cases, we find that our approach results in better density estimates than a commonly used baseline. We also show how our housing density estimator can be used to classify buildings as residential or non-residential.
AB - We propose a neural network component, the regional aggregation layer, that makes it possible to train a pixel-level density estimator using only coarse-grained density aggregates, which reflect the number of objects in an image region. Our approach is simple to use and does not require domain-specific assumptions about the nature of the density function. We evaluate our approach on several synthetic datasets. In addition, we use this approach to learn to estimate high-resolution population and housing density from satellite imagery. In all cases, we find that our approach results in better density estimates than a commonly used baseline. We also show how our housing density estimator can be used to classify buildings as residential or non-residential.
KW - Dasymetric mapping
KW - Population density
KW - Remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85058635628&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058635628&partnerID=8YFLogxK
U2 - 10.1145/3274895.3274934
DO - 10.1145/3274895.3274934
M3 - Conference contribution
AN - SCOPUS:85058635628
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 33
EP - 42
BT - 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
A2 - Xiong, Li
A2 - Tamassia, Roberto
A2 - Banaei, Kashani Farnoush
A2 - Guting, Ralf Hartmut
A2 - Hoel, Erik
T2 - 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2018
Y2 - 6 November 2018 through 9 November 2018
ER -