A classifier to detect best mode for Solar Chimney Power Plant system

Abdelsalam, Emad; Darwish, Omar; Karajeh, Ola; Almomani, Fares; Darweesh, Dirar; Kiswani, Sanad; Omar, Abdul Hafidz; Kisrawi, Malek

doi:10.1016/j.renene.2022.07.056

Cited by 7 publications

(1 citation statement)

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“…Environmental monitoring is facilitated by tracking deforestation, urbanization patterns, wetland losses, and ecological shifts. Disaster response efforts rely on LCCD to rapidly assess damage from events like floods, earthquakes and wildfires [57,58]. Urban planners leverage LCCD to guide sustainable development, zoning policies, and infrastructure planning aligned with landscape dynamics.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A Generic and Extendable Framework for Benchmarking and Assessing the Change Detection Models

Hassouna,

Ismail,

Alqahtani

et al. 2024

Preprint

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Change Detection (CD) of aerial images refers to identifying and analyzing changes between two or more aerial images of the same location taken at different times. The CD is a highly challenging task due to the need to distinguish relevant changes, such as urban expansion, deforestation, or post-disaster damage assessment, from irrelevant ones, such as light conditions, shadows, and seasonal variations. Many CD papers have recently been published, where most of the papers that proposed a new model contained a comparison between their proposed and state-of-the-art (SOTA) models. While many recent studies propose new deep learning (DL) models for improving CD performance, their comparative analyses are often restricted, lacking comprehensive insights into the proposed models' real-world generalizability, robustness, and performance trade-offs across diverse change characteristics. This paper presents a novel generic framework to systematically benchmark and assess DL-based CD models through three parallel pipelines: 1) cross-testing models on diverse benchmark datasets to evaluate generalization, 2) robustness analysis against different image corruptions, and 3) multi-faceted contour-level analytics evaluating detection sensitivity to change size/complexity. The framework is applied to comparatively evaluate five state-of-the-art DL-based CD models - Changeformer, BIT, Tiny, SNUNet, and CSA-CDGAN. Extensive experiments unveil each model's strengths, limitations and biases, highlighting their relative proficiencies in generalizing across data distributions, resilience to noise corruption, and discriminative capabilities for changes of varying characteristics. The proposed benchmarking framework demonstrates significant potential for guiding the selection of suitable CD models tailored to specific application requirements by comprehensively evaluating their generalizability, robustness, and detection capabilities across diverse real-world scenarios. This systematic evaluation approach can drive future research into developing more robust and versatile CD solutions aligned with practical needs.

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Section: Background and Literature Reviewmentioning

confidence: 99%