摘要(中) 遙測衛星影像具有周期性及面積廣闊的觀測資訊, 常用於分析地球各環境現象的發展及
變化. 近年來, 隨著深度學習技術的進步, 許多人工神經網路(Artificial Neural Networks, ANNs) …

摘要(中) 人工神經網路(ANN) 廣泛用於多種用途, 其中之一是衛星影像的地表覆蓋分類. 然而,
人們對人工神經網路如何在輸入和輸出之間建立聯繫知之甚少. 本研究的目的是獲得基於ANN …

In this paper, we investigate the use of Explainable Artificial Intelligence (XAI) methods for
the interpretation of two Convolutional Neural Network (CNN) classifiers in the field of …

This work proposes an interpretable Deep Learning framework utilizing Vision Transformers
(ViT) for the classification of remote sensing images into land use and land cover (LULC) …

本研究係應用監督性類神經網路(Supervised Neural Network) 之理論, 發展萃取(extract)
衛星影像特徵值(feature) 之程式介面, 以進行遙測影像之分類. 在運用之技術上 …

AI-driven precision agriculture applications can benefit from the large data source that
remote sensing (RS) provides, as it can gather agricultural monitoring data at various scales …

Urban vegetation mapping is critical in many applications, ie, preserving biodiversity,
maintaining ecological balance, and minimizing the urban heat island effect. It is still …

The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-
theart machine learning methods for exploratory and predictive analysis of satellite imagery …

The agricultural landscape can be interpreted at different semantic levels, such as fine low-
level crop (LLC) classes (eg, Wheat, Almond, and Alfalfa) and broad high-level crop (HLC) …

The rapid processing, analysis, and mining of remote-sensing big data based on intelligent
interpretation technology using remote-sensing cloud computing platforms (RS-CCPs) have …