Current and future applications of statistical machine learning algorithms for agricultural machine vision systems

TU Rehman, MS Mahmud, YK Chang, J Jin… - … and electronics in …, 2019 - Elsevier
With being rapid increasing population in worldwide, the need for satisfactory level of crop
production with decreased amount of agricultural lands. Machine vision would ensure the …

Land use change modelling: current practice and research priorities

PH Verburg, PP Schot, MJ Dijst, A Veldkamp - GeoJournal, 2004 - Springer
Land use change models are tools to support the analysis of the causes and consequences
of land use dynamics. Scenario analysis with land use models can support land use …

[HTML][HTML] Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model

A Abdollahi, B Pradhan - Science of the Total Environment, 2023 - Elsevier
One of the worst environmental catastrophes that endanger the Australian community is
wildfire. To lessen potential fire threats, it is helpful to recognize fire occurrence patterns and …

[HTML][HTML] Urban land-use change: The role of strategic spatial planning

AM Hersperger, E Oliveira, S Pagliarin, G Palka… - Global Environmental …, 2018 - Elsevier
To date land-change science has devoted little attention to spatial policy and planning in
urban landscapes despite the widely accepted premise that planning affects urban land …

Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment

RG Pontius Jr, M Millones - International journal of remote sensing, 2011 - Taylor & Francis
The family of Kappa indices of agreement claim to compare a map's observed classification
accuracy relative to the expected accuracy of baseline maps that can have two types of …

A Google Earth Engine approach for wildfire susceptibility prediction fusion with remote sensing data of different spatial resolutions

S Tavakkoli Piralilou, G Einali, O Ghorbanzadeh… - Remote sensing, 2022 - mdpi.com
The effects of the spatial resolution of remote sensing (RS) data on wildfire susceptibility
prediction are not fully understood. In this study, we evaluate the effects of coarse (Landsat 8 …

Modeling land use change using cellular automata and artificial neural network: The case of Chunati Wildlife Sanctuary, Bangladesh

K Islam, MF Rahman, M Jashimuddin - Ecological indicators, 2018 - Elsevier
Land use changes generally affect the integrity of an ecosystem. The effect of this change
can be very severe if the conversion disrupts a crucial habitat of major plants and animals …

Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea

S Park, C Choi, B Kim, J Kim - Environmental earth sciences, 2013 - Springer
Every year, the Republic of Korea experiences numerous landslides, resulting in property
damage and casualties. This study compared the abilities of frequency ratio (FR), analytic …

Detecting important categorical land changes while accounting for persistence

RG Pontius Jr, E Shusas, M McEachern - Agriculture, ecosystems & …, 2004 - Elsevier
The cross-tabulation matrix is a fundamental starting point in the analysis of land change,
but many scientists fail to analyze the matrix according to its various components and thus …

Predicting land-use change

A Veldkamp, EF Lambin - Agriculture, ecosystems & environment, 2001 - Elsevier
Land use change modelling, especially if done in a spatially-explicit, integrated and multi-
scale manner, is an important technique for the projection of alternative pathways into the …