Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond
In recent years, deep learning has significantly reshaped numerous fields and applications,
fundamentally altering how we tackle a variety of challenges. Areas such as natural …
fundamentally altering how we tackle a variety of challenges. Areas such as natural …
Artificial intelligence for climate change biology: from data collection to predictions
O Levy, S Shahar - Integrative and Comparative Biology, 2024 - academic.oup.com
In the era of big data, ecological research is experiencing a transformative shift, yet big-data
advancements in thermal ecology and the study of animal responses to climate conditions …
advancements in thermal ecology and the study of animal responses to climate conditions …
[HTML][HTML] Fostering deep learning approaches to evaluate the impact of urbanization on vegetation and future prospects
Vegetation is an essential component of our global ecosystem and an important indicator of
the dynamics and productivity of land cover. Vegetation forecasting research has been …
the dynamics and productivity of land cover. Vegetation forecasting research has been …
Land use classification of high resolution remote sensing images using an encoder based modified GAN architecture
The development of new deep learning algorithms has brought in a significant change in
land use classification. Earlier models in remote sensing image classification were mainly …
land use classification. Earlier models in remote sensing image classification were mainly …
[HTML][HTML] WildARe-YOLO: A lightweight and efficient wild animal recognition model
For the protection of endangered species and successful wildlife population monitoring, wild
animal recognition is essential. While deep learning models like YOLOv5 have shown …
animal recognition is essential. While deep learning models like YOLOv5 have shown …
Deep-sdm: A unified computational framework for sequential data modeling using deep learning models
Deep-SDM is a unified layer framework built on TensorFlow/Keras and written in Python
3.12. The framework aligns with the modular engineering principles for the design and …
3.12. The framework aligns with the modular engineering principles for the design and …
Evaluating the method reproducibility of deep learning models in the biodiversity domain
W Ahmed, VK Kommineni, B König-Ries… - ar**
Estimation of aquatic ecosystem health indices can assist in reducing the burden of time-
consuming, labor-intensive, and cost-effective fieldwork for the sustainable evaluation of …
consuming, labor-intensive, and cost-effective fieldwork for the sustainable evaluation of …
Predicting species distributions with environmental time series data and deep learning
Species distribution models (SDMs) are widely used to gain ecological understanding and
guide conservation decisions. These models are developed with a wide variety of algorithms …
guide conservation decisions. These models are developed with a wide variety of algorithms …
[PDF][PDF] Interreligious Views on the Integration of Artificial Intelligence and Indigenous Knowledge for Environmental Preservation
JN Molino - Religion and Social Communication Journal of the, 2023 - researchgate.net
This paper employs a qualitative narrative analysis to explore interreligious perspectives on
integrating Artificial Intelligence (AI) and Indigenous Knowledge (IK) to address …
integrating Artificial Intelligence (AI) and Indigenous Knowledge (IK) to address …