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Geospatial Data Disaggregation through Self-Trained Encoder–Decoder Convolutional Models
2021
ISPRS International Journal of Geo-Information
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of ancillary data. ...
Still, the data are often only released for highly aggregated geospatial areas, which can mask important local hotspots. ...
The funders also had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. ...
doi:10.3390/ijgi10090619
fatcat:qri44qcovjfw7n3o3z67f4ru64
Improving Urban Population Distribution Models with Very-High Resolution Satellite Information
2019
Data
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These weighting layers are created from MR and/or VHR input data, using simple built-up proportion or reallocation "weights", obtained from a set of multiple ancillary data used to train a Random Forest ...
its use when mapping urban populations. ...
Acknowledgments: The authors gratefully thanks the ASSESS project (http://assess-sn.org/), funded by the ARES-CDD (https://www.ares-ac.be), which provided the access to the population data. ...
doi:10.3390/data4010013
fatcat:f7llmwtg6rcvlhsc5mrlh7rzrm
Disaggregating Population Data and Evaluating the Accuracy of Modeled High-Resolution Population Distribution—The Case Study of Germany
2020
Sustainability
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The article presents a dasymetric-based approach for modeling high-resolution population data based on urban density, dispersion, and land cover/use. ...
With data on urban density, a relative deviation between the modeled and actual population of 14.1% is achieved. Data on land cover/use reduces the deviation to 12.4%. ...
Author Contributions: S.E. is the sole author and responsible for all sections of the article. All authors have read and agreed to the published version of the manuscript. ...
doi:10.3390/su12103976
fatcat:crzt77ajqnflhb2pubac7nbx5u
Local Population Mapping Using a Random Forest Model Based on Remote and Social Sensing Data: A Case Study in Zhengzhou, China
2020
Remote Sensing
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This study used a random forest algorithm, on the basis of remote sensing (i.e., satellite imagery) and social sensing data (i.e., point-of-interest and building footprint), to disaggregate census population ...
High-resolution gridded population data are important for understanding and responding to many socioeconomic and environmental problems. ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...
doi:10.3390/rs12101618
fatcat:hwbq5e2vpfem5l526jkjf27mj4
Using satellite imagery to understand and promote sustainable development
[article]
2020
arXiv
pre-print
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We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of models' predictive ...
We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine learning. ...
Data and code for replication of all results will be made public upon publication. ...
arXiv:2010.06988v1
fatcat:c2sx7ftscncofjjyibyyszqztu
Census-independent population mapping in northern Nigeria
2018
Remote Sensing of Environment
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A B S T R A C T Although remote sensing has long been used to aid in the estimation of population, it has usually been in the context of spatial disaggregation of national census data, with the census ...
Used in concert with previously published demographic estimates, our population estimates allowed for predictions of the population under 5 in ten administrative wards that fit strongly with reference ...
In this paper, we tackle the problem of unreliable and outdated census population counts through a bottom-up population mapping approach that couples semi-automated high-resolution settlement mapping with ...
doi:10.1016/j.rse.2017.09.024
pmid:29302127
pmcid:PMC5738969
fatcat:7hovtlbgsfh3xltpopfk2pydf4
Unmixing Aggregate Data: Estimating the Social Composition of Enumeration Districts
1998
Environment and planning A
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An approach to 'unmixing' aggregate data, and thus to revealing the nature of the subunit variation masked by aggregation, is introduced. ...
In this paper the authors address the problem of interpreting and classifying aggregate data sources and draw parallels between tasks commonly encountered in image processing and census analysis. ...
SAS-like data are then derived from these synthetic ED populations and used to train an ANN to model the proportional presence of each household group. ...
doi:10.1068/a301929
fatcat:46dwwhehzzfe7c3dzbav3qvepa
An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification
2017
Remote Sensing
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This study presents the development of a semi-automated processing chain for urban object-based land-cover and land-use classification. ...
For automation purposes, we developed two GRASS GIS add-ons enabling users (1) to optimize segmentation parameters in an unsupervised manner and (2) to classify remote sensing data using several individual ...
The 'buildings' class is of particular importance in the context of the MAUPP and SmartPop projects since their final objective is to disaggregate census population data using LULC maps in order to model ...
doi:10.3390/rs9040358
fatcat:q65vsz34trfyxd6ntuu6lxgxca
NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review
2019
Energies
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The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes ...
As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics. ...
optimization and machine learning (supervised and unsupervised) algorithms used for load classification. ...
doi:10.3390/en12112203
fatcat:kvwy37rhkjethenc25i7kmc3ya
Improvement of a Dasymetric Method for Implementing Sustainable Development Goal 11 Indicators at an Intra-Urban Scale
2021
Remote Sensing
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Updated high-resolution population density and settlement maps are the basic input products for such indicators and their sub-indicators. ...
The novelties of our paper include the introduction of building height information and site-specific weight values for population density correction. ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...
doi:10.3390/rs13142835
fatcat:nahnmw6pajfwxi73iuajicz5bu
Mapping Long-Term Dynamics of Population and Dwellings Based on a Multi-Temporal Analysis of Urban Morphologies
2018
ISPRS International Journal of Geo-Information
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For this purpose, an approach for the automatic derivation of such information is presented. ...
However, as various different of approaches to estimate the current distribution of population and dwellings exists, further evidence on past dynamics is needed for a better understanding of urban processes ...
(Landesamt für Vermessung und Geobasisinformation, LVermGeo) for the provision of this data. ...
doi:10.3390/ijgi8010002
fatcat:6n4qplapxnbpfko4gpvt75pmha
Abstracts from the 9th DACH+ Conference on Energy Informatics
2020
Energy Informatics
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Energy Informatics 2020, 3(Suppl 2):P1 Summary A successful deployment and operation of smart grids depends on the reliability and security of the protocols used to gather data from the various components ...
Based on a structured process for fuzzing in this specific domain we develop a fuzzer that has been made publicly available to ensure repeatability of the results and ease further security assessments ...
supervising and supporting the PhD work. ...
doi:10.1186/s42162-020-00113-9
fatcat:dgbgi6ybzjextfsllsuv4wqokq
Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling
2021
ISPRS International Journal of Geo-Information
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In this paper, we present a workflow for deep learning-based building stock modeling using aerial images at a city scale for heat demand modeling. ...
The results for DL-building extraction exhibit F1-accuracies of 87%, and construction types yield an overall accuracy of 96%. ...
Acknowledgments: The authors would like to thank Nils Dering from Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen (LANUV) for the provision of reference data. ...
doi:10.3390/ijgi10010023
fatcat:mk3xbkpocfemrmuojmfz35t6a4
2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS 2020 1905-1916 Demonstration and Analysis of an Extended Adaptive General Four-Compo-Disaggregating County-Level Census Data for Population Mapping Using Residential Geo-Objects With Multisource ...
., +, JSTARS 2020
4214-4228
Data mining
Disaggregating County-Level Census Data for Population Mapping Using
Residential Geo-Objects With Multisource Geo-Spatial Data. ...
A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020 ...
doi:10.1109/jstars.2021.3050695
fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
An Informed Forensics Approach to Detecting Vote Irregularities
2015
Political Analysis
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This approach allows us to arbitrate between the relative importance of different forensic and contextual features for identifying electoral fraud and results in a diagnostic tool that can be relatively ...
We deploy a Bayesian additive regression trees (BART) model–a machine-learning technique–on a large cross-national data set to explore the dense network of potential relationships between various forensic ...
Finally, the advantage of semi-supervised machine learning algorithms such as BART is that they are able to uncover complex relationships between sets of covariates and outcomes of interest. ...
doi:10.1093/pan/mpv023
fatcat:klbg5b57ivayffcczxqvuo35xm
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