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Monitoring Coal Mine Reclamation Compliance Using Deep Learning Analysis on Multitemporal Satellite Imagery

title: Monitoring Coal Mine Reclamation Compliance Using Deep Learning Analysis on Multitemporal Satellite Imagery abstract: 監測煤礦開採與復育活動對於確保環境問責與推動永續資源管理至關重要。遙感技術提供了一種強大的方法，能夠在無需大量實地調查的情況下觀測大範圍的地表變化。本論文提出了一個多時期深度學習框架，利用衛星影像系統性地監測並評估印尼南加里曼丹地區煤礦復育的合規情況。該研究分為兩個階段，反映了從初步研究到後續研究的方法進展。在第一階段，整合了 Sentinel-2 多光譜影像，使用 U-Net 分割模型來分類礦區與非礦區。從礦區轉變為非礦區的區域被解釋為復育地。雖然此方法能有效偵測地表擾動與恢復的整體模式，但將所有裸地都歸類為礦區，限制了其區分不同復育階段或具體地表狀況的能力。為了解決這一限制，第二階段通過僅使用 Sentinel-2 影像，結合多種光譜指數和綜合公式來改進該框架。利用基於 U-Net 的深度學習模型，將五種地表組成進行分類，包括表土層、次表土層、植被、煤層和水體，整體分類準確率達到 0.94，Kappa係數為 0.91。這一細緻的分類使得礦區與復育過程的追蹤更加精確，能夠識別次表土層暴露為開採活動，並將次表土層或煤層轉變為植被或表土層視為復育進展。從 2016 年到 2021 年的時序分析顯示，2019 年礦區大幅擴展，隨後在 2020 年復育活動顯著增加。將分類結果與煤礦許可邊界進行整合後，計算出合規比率（CR）介於 0.32至 1.44之間，反映了九個礦區許可持有者之間的差異。同時，還建立了復育活動指數（RAI），作為一種簡單的量化比較方法，用以檢驗其年度趨勢是否與深度學習導出的地表變化一致，結果顯示高度相關。總體而言，本研究提出的多時期深度學習框架證明了將衛星遙測與空間分析方法相結合，在礦區與復育監測方面的準確性、可擴展性與透明性，為基於資料的環境治理提供了有力支持。;Monitoring coal mining and reclamation activities is essential for ensuring environmental accountability and sustainable resource management. Remote sensing provides a powerful means of observing large scale land surface changes without requiring extensive field surveys. This dissertation develops a multitemporal deep learning framework using satellite imagery to systematically monitor and assess reclamation compliance in coal mining regions of South Kalimantan, Indonesia. The research is conducted in two stages, reflecting methodological advancements from the initial to the subsequent study. In the first stage, Sentinel-2 multispectral imagery were integrated to classify mining and non-mining areas with U-Net segmentation. Changes from mining to non-mining areas were interpreted as reclamation. While this approach effectively detected general patterns of surface disturbance and recovery, it generalized all barren land as mining, limiting its ability to distinguish detailed reclamation stages or specific surface conditions. To address this limitation, the second stage refined the framework by employing only Sentinel-2 imagery with multiple spectral indices and composite formulations. A U-Net based deep learning model was trained to classify five surface components topsoil, subsoil, vegetation, coal bodies, and water bodies with an overall accuracy of 0.94 and a Kappa coefficient of 0.91. This detailed classification enabled more precise tracking of mining and reclamation processes, identifying subsoil exposure as mining activity and transitions from subsoil or coal bodies to vegetation or topsoil as reclamation progress. Temporal analysis from 2016 to 2021 revealed substantial mining expansion in 2019, followed by a sharp increase in reclamation activity in 2020. The integration of classification results with coal mining permit boundaries produced CRs (CR) ranging from 0.32 to 1.44, reflecting variations among nine permit holders. A RAI was also developed as a simple quantitative comparison method to examine whether its annual trends align with the deep learning derived spatial changes, showing strong correspondence. Overall, the proposed multitemporal framework demonstrates the effectiveness of combining satellite based deep learning and spatial analytics for accurate, scalable, and transparent monitoring of mining and reclamation dynamics, supporting data-driven environmental governance.

整合型除水濃縮氣相層析系統開發;Development of an Integrated Dehydration and Concentration Gas Chromatography System

title: 整合型除水濃縮氣相層析系統開發;Development of an Integrated Dehydration and Concentration Gas Chromatography System abstract: 本計畫旨在開發一套高靈敏度且即時的揮發性有機化合物（VOCs）監測系統，結合創新除水技術與熱脫附預濃縮，並採用心切氣相層析分離方法，以提升空氣品質監測的準確度與效率。透過整合先進的氣體流路控制與雙重偵測器配置，系統可有效分析多種複雜成分，尤其針對工業區及交通樞紐等污染密集地區，此系統將有助於環境污染源快速辨識與即時管控，提升監測數據的科學性與應用價值，同時培育專業技術人才，促進產學合作與技術產業化。在社會影響方面將提升空氣污染監測的精準度與時效，有助於環境健康風險的及時評估與預警，保障民眾健康，促進公共衛生改善，透過有效監控污染源排放，促進政府環境政策的科學決策與落實，增強社會對環境保護的認知與參與。

整治場址數位監測與高精度汙染團層析技術於抽出處理及淋洗策略之應用(第2年);Application of smart sensing and plume detection models to improve efficiency of in-situ pump and treat and flushing remediation technologies(Year 2)

title: 整治場址數位監測與高精度汙染團層析技術於抽出處理及淋洗策略之應用(第2年);Application of smart sensing and plume detection models to improve efficiency of in-situ pump and treat and flushing remediation technologies(Year 2) abstract: 本計畫以佶鼎科技股份有限公司廠址作為模場試驗場域，驗證與優化土壤及地下水污染整治技術。調查結果顯示，地下水中銅(Cu)與鎳(Ni)污染分布面積分別為507 及 811.2 平方公尺，另有多口監測井檢出鉛(Pb)濃度超標，顯示場址具多重重金屬污染特性。主要採用抽出處理法(Pump and Treat)控制污染，雖整體濃度呈下降趨勢，仍有局部監測井超標。為提升整治效能，輔以現地土壤淋洗法(In-situ SoilFlushing)，促進土壤中金屬脫附並抽取處理，並已完成模場試驗。場址含水層由多層砂質與黏土交互組成，具高度異質性， 7~8 公尺處之黏土層形成滯留屏障，使污染物分佈不均，抽出與灌注效率受限。此外，地下水流向受降雨與鄰近抽水行為影響，流場變異大，增加整治操作與成效評估之挑戰。因此，本模場計畫規劃共兩年研究期程，第一年提出技術需求與獲得研究成果，包括： (1)即時地下水位與基礎水質觀測以強化整治策略規劃； (2)評估含水層水力傳導係數空間分布以增進抽注作業效率； (3)運用光纖高解析監測技術評估垂直向導水特性及水流通量。為強化地質分層與材料分布之空間解析，提高藥劑注入與抽出過程效率。本年度將以第一年之觀測資料與試驗成果為基礎，導入長短期記憶網路(Long Short-Term Memory, LSTM)結合退火演算法(Simulated Annealing, SA)之深度學習架構，進行污染物濃度時空變化的預測與溯源分析。LSTM 模型能有效捕捉地下水污染傳輸中具非線性與時序依賴的特性，藉由大量歷史水位、水質與抽灌操作資料之訓練，預測未來污染濃度與趨勢；退火演算法則用於模型參數與初始權重之全域優化，以避免深度學習陷入局部極值，提升預測穩定度與泛化能力。此組合技術可同時進行「污染來源區反演」與「整治策略模擬」，透過比對模擬結果與監測資料差異，不斷迭代修正模型參數，最終建立污染源時空分布的最佳化反演結果。整體技術具備自動化訓練流程與高效參數搜尋能力，可有效提升多重污染(Cu、 Ni、 Pb)傳輸之預測精度，協助識別主要污染來源、評估整治成效，並提供後續決策支援。 ;This project designates the Gi Ding Technology Co., Ltd. industrial site as a pilot-scale test field to validate and optimize soil and groundwater contamination remediation technologies. Site investigations indicate that copper (Cu) and nickel (Ni) contamination in groundwater cover areas of approximately 507 m² and 811.2 m², respectively. In addition, multiple monitoring wells exhibit lead (Pb) concentrations exceeding regulatory standards, demonstrating the presence of multi-metal contamination at the site. Pump-and-treat remediation has been implemented as the primary control measure, and although overall contaminant concentrations show a declining trend, exceedances persist at several localized monitoring points. To enhance remediation efficiency, in-situ soil flushing has been employed as a supplementary technique to promote desorption of metals from the soil matrix followed by extraction and treatment of contaminated groundwater. Pilot-scale testing of this approach has been completed. The site aquifer system consists of interbedded sandy and clayey layers with pronounced heterogeneity. A clay layer at depths of approximately 7–8 m acts as a semi-confining barrier, resulting in uneven contaminant distribution and limiting the effectiveness of extraction and injection operations. Furthermore, groundwater flow directions are strongly influenced by precipitation events and nearby pumping activities, leading to highly variable flow fields and increased uncertainty in remediation operation and performance evaluation. Accordingly, the pilot study is structured over a two-year research period. In the first year, technical needs were identified and key outcomes were achieved, including: (1) implementation of real-time groundwater level and baseline water quality monitoring to strengthen remediation strategy development; (2)assessment of the spatial variability of aquifer hydraulic conductivity to improve the efficiency of pumping and injection operations; and (3) application of high-resolution fiber-optic sensing techniques to evaluate vertical hydraulic connectivity and groundwater fluxes. These efforts aim to refine the spatial resolution of stratigraphic layering and material distribution, thereby enhancing the effectiveness of chemical injection and extraction processes. Building upon the observational data and experimental results obtained in the first year, the second-year research will integrate a deep learning framework combining Long Short-Term Memory (LSTM) networks with a Simulated Annealing (SA) algorithm to predict the spatiotemporal evolution of contaminant concentrations and to conduct source identification analyses. LSTM models are well suited to capture the nonlinear behavior and temporal dependencies inherent in groundwater contaminant transport. Trained on extensive historical datasets of ground water levels, water quality measurements, and pumping–injection operations, the model will forecast future contaminant concentrations and trends. The simulated annealing algorithm will be employed for global optimization of model parameters and initial network weights, mitigating the risk of convergence to local minima and improving prediction robustness and generalization performance. This integrated approach enables simultaneous “contaminant source inversion” and “remediation strategy simulation.” By iteratively minimizing discrepancies between simulated outputs and observed monitoring data, model parameters are progressively refined to derive an optimized reconstruction of the spatiotemporal distribution of contamination sources. Overall, the proposed methodology features an automated training workflow and efficient parameter search capability, substantially enhancing predictive accuracy for multi-metal contaminant transport (Cu, Ni, Pb), supporting identification of primary contamination sources, evaluation of remediation effectiveness, and provision of robust decision support for subsequent site management.

資訊教育學門規劃研究推動計畫;Steering Project of the Information Education Discipline for Research Development

title: 資訊教育學門規劃研究推動計畫;Steering Project of the Information Education Discipline for Research Development abstract: 本計畫旨在因應資訊科技迅速發展及新興科技對教育之衝擊與影響，強化資訊教育學門之研究與實踐能量。計畫以「強化研發能力、加速成果擴散、培育與延攬人才、及深化國際交流」為核心目標，推動前瞻研究與社會實踐之結合，並回應臺灣 2030 科技願景及國科會人文處相關政策方向。預期本計畫將在人文與社會層面，促進資訊教育之實務應用與推廣；在經濟層面，推動資訊教育創新與人才培育，強化我國在數位教育產業之競爭力；在學術層面，凝聚研究社群能量，提升資訊教育學門之研究品質與能見度，擴大跨域合作與國際影響力。