@article{JOCHEM2018104,

title = {Identifying residential neighbourhood types from settlement points in a machine learning approach},

author = {Warren C. Jochem and Tomas J. Bird and Andrew J. Tatem},

url = {https://www.sciencedirect.com/science/article/pii/S0198971517304210},

doi = {https://doi.org/10.1016/j.compenvurbsys.2018.01.004},

issn = {0198-9715},

year  = {2018},

date = {2018-01-01},

journal = {Computers, Environment and Urban Systems},

volume = {69},

pages = {104-113},

abstract = {Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. Far less attention has been given to using geospatial vector data (i.e. points, lines, polygons) to map land uses. This paper presents an approach to distinguish residential settlement types (regular vs. irregular) using an existing database of settlement points locating structures. Nine data features describing the density, distance, angles, and spacing of the settlement points are calculated at multiple spatial scales. These data are analysed alone and with five common remote sensing measures on elevation, slope, vegetation, and nighttime lights in a supervised machine learning approach to classify land use areas. The method was tested in seven provinces of Afghanistan (Balkh, Helmand, Herat, Kabul, Kandahar, Kunduz, Nangarhar). Overall accuracy ranged from 78% in Kandahar to 90% in Nangarhar. This research demonstrates the potential to accurately map land uses from even the simplest representation of structures.},

keywords = {Big data, Land use, Machine learning, Point pattern analysis, Texture, Urban morphology},

pubstate = {published},

tppubtype = {article}

}

@article{TATEM200442,

title = {Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery},

author = {Andrew J. Tatem and Abdisalan M. Noor and Simon I. Hay},

url = {https://www.sciencedirect.com/science/article/pii/S0034425704001944},

doi = {https://doi.org/10.1016/j.rse.2004.06.014},

issn = {0034-4257},

year  = {2004},

date = {2004-01-01},

journal = {Remote Sensing of Environment},

volume = {93},

number = {1},

pages = {42-52},

abstract = {This paper presents an appraisal of satellite imagery types and texture measures for identifying and delineating settlements in four Districts of Kenya chosen to represent the variation in human ecology across the country. Landsat Thematic Mapper (TM) and Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) imagery of the four districts were obtained and supervised per-pixel classifications of image combinations tested for their efficacy at settlement delineation. Additional data layers including human population census data, land cover, and locations of medical facilities, villages, schools and market centres were used for training site identification and validation. For each district, the most accurate approach was determined through the best correspondence with known settlement and non-settlement pixels. The resulting settlement maps will be used in combination with census data to produce medium spatial resolution population maps for improved public health planning in Kenya.},

keywords = {JERS-1 SAR, Kenya, Landsat TM, Neural network, Population, Public health, Settlement mapping, Texture},

pubstate = {published},

tppubtype = {article}

}