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Evaluation of conditioned Latin hypercube sampling for soil mapping based on a machine learning method
Conditioned Latin hypercube sampling Soil mapping Representativeness Sample randomness
2024/1/12
Sampling design plays an important role in soil survey and soil mapping. Conditioned Latin hypercube sampling (cLHS) has been proven as an efficient sampling strategy and used widely in digital soil m...
Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms
precision agriculture field crops machine learning deep learning image processing textural features
2023/12/21
The current mainstream approach of using manual measurements and visual inspections for crop lodging detection is inefficient, time-consuming, and subjective. An innovative method for wheat lodging de...
Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms-Case Study of Evergreen Conifer Planted Forests in Japan
UAS stem volume TLS SAR random forest support vector multiple regression forest biophysical parameter
2023/12/21
The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing...
Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China
Water resources Remote sensing Total nitrogen Hyperspectral imagery Machine learning Bootstrap
2023/12/19
In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality paramete...
Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches
land water segmentation remote sensing deep learning sparse labels
2023/12/5
Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife...
Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States
LiDAR airborne laser scanning enhanced forest inventory aboveground biomass forest carbon deep learning Maine New Hampshire Vermont Massachusetts Connecticut Rhode Island
2023/12/5
Light detection and ranging (LiDAR) has become a commonly-used tool for generating remotely-sensed forest inventories. However, LiDAR-derived forest inventories have remained uncommon at a regional sc...
Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning
machine learning ratio-based indices orthogonal indices Koppen–Geiger climate regionalization landscape change remote sensing landcover
2023/12/4
Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural...
Forest Farm Fire Drone Monitoring System Based on Deep Learning and Unmanned Aerial Vehicle Imagery
Forest Farm Fire Monitoring System Deep Learning
2023/12/1
Forest fires represent one of the main problems threatening forest sustainability. Therefore, an early prevention system of forest fire is urgently needed. To address the problem of forest farm fire m...
Wildfire Risk Assessment in Liangshan Prefecture, China Based on An Integration Machine Learning Algorithm
frequency ratio MCD64A1 Bayesian optimization support vector machine random forest extreme gradient boosting
2023/11/30
Previous wildfire risk assessments have problems such as subjectivity of weight allocation and the linearization of statistical models, resulting in generally low robustness and low generalization abi...
苏州大学计算机科学与技术学院杨壮老师的论文在机器学习顶级期刊Journal of Machine Learning Research （JMLR）上在线发表（图）
杨壮 机器学习 Journal of Machine Learning Research
2024/2/3
苏州大学计算机科学与技术学院杨壮副教授撰写的论文“Improved Powered Stochastic Optimization Algorithms for Large-Scale Machine Learning”在机器学习顶级期刊Journal of Machine Learning Research（JMLR）上在线发表，杨壮副教授为该论文的唯一作者，苏州大学计算机科学与技术学院为该论文...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Distributed sequential federated learning
分布式 联合学习 数据分析
2023/11/7
The analysis of data stored in multiple sites has become more popular, raising new concerns about the security of data storage and communication. Federated learning, which does not require centralizin...
医学部汪天富、雷柏英教授团队在《IEEE Transactions on Neural Networks and Learning Systems》上发表文章（图）
雷柏英教授 生物医学工程 深圳大学医学部
2023/10/23
近期，深圳大学医学部生物医学工程学院汪天富、雷柏英教授团队的研究成果《MHW-GAN: Multi-discriminator Hierarchical Wavelet Generative Adversarial Network for Multi-modal Image Fusion》在顶级期刊IEEE Transactions on Neural Networks and Learning ...
深圳大学医学部汪天富教授团队在《IEEE Transactions on Neural Networks and Learning Systems》上发表系列重要文章（图）
生物医学工程 汪天富教授 深圳大学医学部
2023/10/23
近日，医学部生物医学工程学院汪天富教授团队在人工智能领域顶级国际期刊《IEEE Transactions on Neural Networks and Learning Systems》（中科院大类一区，TOP期刊，IF:14.255）上发表了系列研究成果。
Machine learning-based atom contribution method for the prediction of surface charge density profiles and solvent design
atom contribution computer-aided molecular design decomposition-based algorithm machine learning
2023/6/19
Solvents are widely used in chemical processes. The use of efficient model-based solvent selection techniques is an option worth considering for rapid identification of candidates with better economic...
On the Interplay Between Deep Learning and Dynamical Systems
Deep Learning Dynamical Systems 北大
2023/6/16
The explosion of spatiotemporal data in the physical world requires new deep learning tools to model complex dynamical systems.