IMMerSe: An integrated methodology for mapping and classifying precarious settlements

Feitosa, Flávia da Fonseca; Vasconcelos, Vitor Vieira; Pinho, Carolina Moutinho Duque de; Silva, Guilherme Frizzi Galdino da; Gonçalves, Gilmara da Silva; Danna, Lana Carolina; Lisboa, Flávia Seixas

doi:10.1016/j.apgeog.2021.102494

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…In our study we detected that 91% (~10 km) of the total AS extent classified as buildings refers to stilt houses -locally known as palafitassituated along estuarine margins in the central region of the SP coast. These are extremely precarious habitations sustained by pillars built over wetlands (Fonseca Feitosa et al 2021). Although substantial, the extent of AS provided by such habitations is underestimated in our study.…”

Section: Coastal Hardening In Spmentioning

confidence: 60%

“…Unlike beachfront locations, these regions have historically been overlooked by the real estate market and are considered permanent preservation areas according to federal environmental law (Moraes 2007). Nonetheless, such territories became main residential areas for low-income and marginalised population, especially in the central region of the SP coast (Fonseca Feitosa et al 2021). To make matters worse, amendments to the environmental law in 2012 paved the way for legitimising occupation of these territories under the guise of public utility and social interest principles (Azevedo and Oliveira 2014).…”

Section: Occupations In Lecz 100m In Spmentioning

confidence: 99%

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Urbanisation on the coastline of the most populous and developed state of Brazil: the extent of coastal hardening and occupations in low-elevation zones

Pardal,

Christofoletti,

Martinez

2024

Anthropocene Coasts

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Worldwide, coastlines have been replaced and altered by hard infrastructures to protect cities and accommodate human activities. In addition, human settlements are common and increasing in lowland areas threatened by coastal risk hazards. These urbanisation processes cause severe socioeconomic and ecological losses which demand policy reforms towards better coastal management and climate resilience. A first step in that direction is to comprehend the status of coastal hardening and occupation of vulnerable areas. Here, we mapped the coastline of the most populous and developed state of Brazil: São Paulo (SP). Our goal was to quantify the linear extent of natural habitats, artificial structures (AS), and occupations in low-elevation coastal zones (≤ 5 m) within 100 m from marine environments (LECZ100m) along the coastline and within estuaries. SP coast has a total extent of 244 km of AS, of which 125 and 119 km correspond to AS running along the coastline (e.g., seawalls, breakwaters) and extending from the shoreline into adjacent waters (e.g., jetties, pontoons, groynes), respectively. 63% of the total extent of AS is located in the most urbanised region. Breakwalls were the most common infrastructure (108 km), followed by jetties and wharves (~40 km each), and aquaculture and fishing apparatus (~24 km). Over 300 km of the SP coastline has inland occupations in LECZ100m: 235 and 67 km are adjacent to sandy beaches and estuarine/river margins, respectively. Coastal hardening is advanced in the central region of SP resulting from intense port activities and armoured shorelines. In other regions, much of coastal urbanisation seems to be driven by secondary usage of the cities, such as real estate development for beach houses and tourism. Our findings suggest that coastal urbanisation poses a major but often neglected source of environmental impact and risk hazards in SP and Brazil.

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Section: Coastal Hardening In Spmentioning

confidence: 60%

Section: Occupations In Lecz 100m In Spmentioning

confidence: 99%

Urbanisation on the coastline of the most populous and developed state of Brazil: the extent of coastal hardening and occupations in low-elevation zones

Pardal,

Christofoletti,

Martinez

2024

Anthropocene Coasts

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“…These cells were used as reference units to aggregate data from various sources. The cell space was created from the IBGE statistical grid [71] using a cell size commonly used to identify urban patterns [72][73][74][75]. We decided to use a cellular grid because this spatial structure can be easily generated for any study area and is the most suitable spatial unit to integrate data from different formats and sources.…”

Section: Creating and Integrating Variables Into Cell Gridsmentioning

confidence: 99%

Identifying Urban and Socio-Environmental Patterns of Brazilian Amazonian Cities by Remote Sensing and Machine Learning

et al. 2023

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Identifying urban patterns in the cities in the Brazilian Amazon can help to understand the impact of human actions on the environment, to protect local cultures, and secure the cultural heritage of the region. The objective of this study is to produce a classification of intra-urban patterns in Amazonian cities. Concretely, we produce a set of Urban and Socio-Environmental Patterns (USEPs) in the cities of Santarém and Cametá in Pará, Brazilian Amazon. The contributions of this study are as follows: (1) we use a reproducible research framework based on remote sensing data and machine learning techniques; (2) we integrate spatial data from various sources into a cellular grid, separating the variables into environmental, urban morphological, and socioeconomic dimensions; (3) we generate variables specific to the Amazonian context; and (4) we validate these variables by means of a field visit to Cametá and comparison with patterns described in other works. Machine learning-based clustering is useful to identify seven urban patterns in Santarém and eight urban patterns in Cametá. The urban patterns are semantically explainable and are consistent with the existing scientific literature. The paper provides reproducible and open research that uses only open software and publicly available data sources, making the data product and code available for modification and further contributions to spatial data science analysis.

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“…No gráfico 1, identifica-se que os limiares ótimos para a classificação de NUI são sempre inferiores a 0,5, especialmente nos modelos "Y = AGSN" (em vermelho). Esse resultado coincide com observações realizadas no âmbito do desenvolvimento da Mappa (Feitosa et al, 2021) e evidencia a limitação de utilização deste valor-padrão (0,5) para a classificação de NUIs ou assentamentos precários. Reforça-se, assim, a recomendação de análise das superfícies de probabilidade somada ao conhecimento local e a outras fontes de dados disponíveis.…”

Section: Marabáunclassified

Núcleos urbanos informais : abordagens territoriais da irregularidade fundiária e da precariedade habitacional

2022

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Este livro traz os resultados de uma pesquisa que buscou superar crônicas e históricas lacunas de informação sobre assentamentos precários no Brasil. Coroando uma parceria do Instituto de Pesquisa Econômica Aplicada (Ipea) com o Ministério do Desenvolvimento Regional (MDR), por meio da Secretaria Nacional de Habitação (SNH), os resultados da Pesquisa de Núcleos Urbanos Informais no Brasil estão aqui presentados e discutidos.

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IMMerSe: An integrated methodology for mapping and classifying precarious settlements

Cited by 7 publications

References 13 publications

Urbanisation on the coastline of the most populous and developed state of Brazil: the extent of coastal hardening and occupations in low-elevation zones

Urbanisation on the coastline of the most populous and developed state of Brazil: the extent of coastal hardening and occupations in low-elevation zones

Identifying Urban and Socio-Environmental Patterns of Brazilian Amazonian Cities by Remote Sensing and Machine Learning

Núcleos urbanos informais : abordagens territoriais da irregularidade fundiária e da precariedade habitacional

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