Prediction of compressive strength of nano-silica concrete by using random forest algorithm

M Nigam, M Verma - Asian Journal of Civil Engineering, 2024 - Springer
The prediction of compressive strength in concrete is an important part of civil engineering
because it influences structural design and durability. Recent improvements have brought …

Insect identification in the wild: The ami dataset

A Jain, F Cunha, MJ Bunsen, JS Cañas, L Pasi… - arXiv preprint arXiv …, 2024 - arxiv.org
Insects represent half of all global biodiversity, yet many of the world's insects are
disappearing, with severe implications for ecosystems and agriculture. Despite this crisis …

FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models

NI Bountos, A Ouaknine, D Rolnick - arXiv preprint arXiv:2312.10114, 2023 - arxiv.org
Forests are an essential part of Earth's ecosystems and natural systems, as well as providing
services on which humanity depends, yet they are rapidly changing as a result of land use …

A systematic review on potential analogy of phytobiomass and soil carbon evaluation methods: Ethiopia insights

Y Gelaye - Open Agriculture, 2024 - degruyter.com
Forests play a crucial role in mitigating the impacts of climate change by sequestering
carbon in their biomass and soil. However, Ethiopia faces the threat of soil carbon emissions …

[PDF][PDF] ADistribution SHIFT BENCHMARK FOR SMALL-HOLDER AGROFORESTRY: DO FOUNDATION MODELS IMPROVE GEOGRAPHIC GENERALIZATION?

S Sachdeva, I Lopez, C Biradar… - The Twelfth International …, 2024 - ml-for-rs.github.io
Recent improvements in deep learning for remote sensing have shown that it is possible to
detect individual trees using high resolution satellite remote sensing data. However, there …

Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannas

E Rolf, L Gordon, M Tambe, A Davies - arXiv preprint arXiv:2411.14354, 2024 - arxiv.org
While advances in machine learning with satellite imagery (SatML) are facilitating
environmental monitoring at a global scale, developing SatML models that are accurate and …

PureForest: A Large-scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forests

C Gaydon, F Roche - arXiv preprint arXiv:2404.12064, 2024 - arxiv.org
Knowledge of tree species distribution is fundamental to managing forests. New deep
learning approaches promise significant accuracy gains for forest mapping, and are …

[PDF][PDF] Forecasting of Crushing Strength of Sustainable Concrete by Employing Deep and Random Forest Machine Learning

PK Tiwari, M Verma - 2024 - researchgate.net
Sustainable concrete is one of the milestone of the concrete industry. This concrete fulfills
the requirements of concrete manufacturing industry such as strengthen, Durability …

[PDF][PDF] Leveraging Prompt-Based Segmentation Models and Large Dataset to Improve Detection of Trees

V Grondin, P Massicotte, M Gaha… - Proceedings of the …, 2024 - assets.pubpub.org
The abundance of unlabeled forest images on the web is a powerful yet untapped resource
to train forestry vision models. Two key challenges limiting the use of these unlabeled …

Insect Identification in the Wild: The AMI Dataset

JS Canas, L Pasi, N Pinoy, F Helsing, JA Russo… - Springer
Insects represent half of all global biodiversity, yet many of the world's insects are
disappearing, with severe implications for ecosystems and agriculture. Despite this crisis …