Résumé
In this paper we investigate a local implementation of Random Forest (RF), named Geographical Random Forest (GRF) to predict population density with Very-High-Resolution Remote Sensing (VHHRS) data. As an independent variable we use population density at the neighborhood level from the 2013 census of Dakar, while as explanatory features, the proportions of three different built-up types in each neighborhood derived from a VHHRS land cover classification. The results demonstrated, that by using an appropriate geographic scale to calibrate GRF, we can maximize prediction accuracy due to the incorporation of spatial heterogeneity in the estimates. Additionally, since GRF is an ensemble of local sub-models, the results can be mapped, highlighting local model performance and other interesting spatial variations. Consequently, GRF is suggested as an interesting exploratory and explanatory technique to model remotely-sensed spatially heterogeneous relationships.
| langue originale | Anglais |
|---|---|
| titre | 2019 Joint Urban Remote Sensing Event (JURSE) |
| Sous-titre | May, 22-24, 2019, Vannes, France |
| Editeur | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronique) | 9781728100098 |
| Les DOIs | |
| état | Publié - 1 mai 2019 |
| Evénement | 2019 Joint Urban Remote Sensing Event, JURSE 2019 - Vannes, France Durée: 22 mai 2019 → 24 mai 2019 |
Série de publications
| Nom | 2019 Joint Urban Remote Sensing Event, JURSE 2019 |
|---|
Une conférence
| Une conférence | 2019 Joint Urban Remote Sensing Event, JURSE 2019 |
|---|---|
| Pays | France |
| La ville | Vannes |
| période | 22/05/19 → 24/05/19 |
Empreinte digitale
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An application of geographical random forests for population estimation in Dakar, Senegal using very-high-resolution satellite imagery. / Georganos, Stefanos; Grippa, Tais; Gadiaga, Assane; Vanhuysse, Sabine; Kalogirou, Stamatis; Lennert, Moritz; Linard, Catherine.
2019 Joint Urban Remote Sensing Event (JURSE): May, 22-24, 2019, Vannes, France. Institute of Electrical and Electronics Engineers Inc., 2019. 8809049 (2019 Joint Urban Remote Sensing Event, JURSE 2019).Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque
TY - GEN
T1 - An application of geographical random forests for population estimation in Dakar, Senegal using very-high-resolution satellite imagery
AU - Georganos, Stefanos
AU - Grippa, Tais
AU - Gadiaga, Assane
AU - Vanhuysse, Sabine
AU - Kalogirou, Stamatis
AU - Lennert, Moritz
AU - Linard, Catherine
PY - 2019/5/1
Y1 - 2019/5/1
N2 - In this paper we investigate a local implementation of Random Forest (RF), named Geographical Random Forest (GRF) to predict population density with Very-High-Resolution Remote Sensing (VHHRS) data. As an independent variable we use population density at the neighborhood level from the 2013 census of Dakar, while as explanatory features, the proportions of three different built-up types in each neighborhood derived from a VHHRS land cover classification. The results demonstrated, that by using an appropriate geographic scale to calibrate GRF, we can maximize prediction accuracy due to the incorporation of spatial heterogeneity in the estimates. Additionally, since GRF is an ensemble of local sub-models, the results can be mapped, highlighting local model performance and other interesting spatial variations. Consequently, GRF is suggested as an interesting exploratory and explanatory technique to model remotely-sensed spatially heterogeneous relationships.
AB - In this paper we investigate a local implementation of Random Forest (RF), named Geographical Random Forest (GRF) to predict population density with Very-High-Resolution Remote Sensing (VHHRS) data. As an independent variable we use population density at the neighborhood level from the 2013 census of Dakar, while as explanatory features, the proportions of three different built-up types in each neighborhood derived from a VHHRS land cover classification. The results demonstrated, that by using an appropriate geographic scale to calibrate GRF, we can maximize prediction accuracy due to the incorporation of spatial heterogeneity in the estimates. Additionally, since GRF is an ensemble of local sub-models, the results can be mapped, highlighting local model performance and other interesting spatial variations. Consequently, GRF is suggested as an interesting exploratory and explanatory technique to model remotely-sensed spatially heterogeneous relationships.
KW - population estimation
KW - random forest
KW - spatial heterogeneity
KW - very-high-resolution remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85072017863&partnerID=8YFLogxK
U2 - 10.1109/JURSE.2019.8809049
DO - 10.1109/JURSE.2019.8809049
M3 - Conference contribution
AN - SCOPUS:85072017863
T3 - 2019 Joint Urban Remote Sensing Event, JURSE 2019
BT - 2019 Joint Urban Remote Sensing Event (JURSE)
PB - Institute of Electrical and Electronics Engineers Inc.
ER -