冰湖识别是了解冰湖对气候变化的响应和评估冰湖溃决洪水潜在危险的先决条件。虽然遥感技术使全球冰湖演变的持续监测和评估成为可能，但准确可靠地提取复杂高原地形区的冰湖仍然具有挑战性。本文提出了融合多源遥感数据和改进后MaskR-CNN深度学习模型的复杂高原地形区冰湖智能识别方法，在MaskR-CNN模型基础上，通过在骨干网络ResNet-50的高层特征（Conv4和Conv5）、FPN的每个特征图以及Mask Head 中引入注意力机制。利用Sentinel-2高分辨遥感影像、ALOS-DEM及NDWI数据组成多波段数据集，并在青藏高原东南部的林芝市进行测试，并进一步比较了改进后Mask R-CNN、U-Net、SegNet和DeepLab V3模型在冰湖识别中的性能。改进后的Mask R-CNN模型具有更高的准确率，模型的精确度、召回率和准确度值分别达到了91.25%、93.69%、92.89%。它有效地降低了山体阴影、湖水浊度和冻融湖水条件对冰湖识别的影响，并显著提高了小冰湖的识别效率。本研究为地形复杂高原地形区冰湖识别提供了可靠解决方案，为深度学习与多源遥感数据结合的智能化冰湖提取提供...

【目的】干涉合成孔径雷达测量(InSAR)技术近年来被广泛用于反演活动层厚度（ALT），然而现有研究较少考虑冻融对地表形变和土壤孔隙水热变化的影响，因此，本文构建了考虑土壤水热变化的ALT反演模型。【方法】使用InSAR技术和CNNBiLSTM-AM模型得到地表参数，顾及冻融驱动下活动层的变形和土壤孔隙及水分的变化构建了活动层厚度反演模型。首先，通过SBAS-InSAR技术提取研究区垂直向地表形变。然后，构建CNN-BiLSTM-AM模型，使用卷积神经网络（Convolutional Neural Networks, CNN）对多源遥感数据特征提取，采用双向长短期记忆网络（Bi-directional Long Short-term Memory,BiLSTM）对提取特征进行预测，添加多头自注意力层（Attention Mechanism, AM）提高模型对关键信息的提取，得到多特征约束下的土壤含水量预测值。最后，以垂直向地表形变作为表征活动层的主要参数，构建基于土壤孔隙比和土壤含水量的活动层厚度反演模型，得到兰新高铁冻土区活动层厚度的时空分布。【结果】模型估计值与俄博岭实测数据验证的...

Agriculture is one of the prime economical sources of India and most of the people directly or indirectly depend on farming. The researchers are focusing on plant ailment detection and managing the imbalanced nutrition in plants. Automation is introduced in agricultural fields and most of these automation strategies use the Internet of Things (IoT) for enhance productivity and automate processes. With the help of several deep and machine learning approaches the endless decision-making performance is performed. Here, the endless decision performance shows appropriate outcomes which helps to solve the unstructured problems in smart farming. It is monitored that the traditional analysis doesn't have enough decision-making ability in the selection of fertilizer quantity that is to be used in farming. This inability leads to crop ailments and that affects the lifestyle of humans too. So, the prior detection of ailments in crops is essential. Enforcing Smart Agriculture is a hot topic in research nowadays to overcome crop damage in the future. Therefore, a new IoT-based smart farming model using deep learning is proposed to increase crop yield. By detecting disease, pests, smart irrigation, and yield, the smart farming model can reduce the amount of water and chemicals used in agriculture. This smart farming model consists of four phases a) disease prediction, b) pest detection c) smart irrigation, and d) yield prediction. In the first phase, the crop images are gathered from online datasets. The diseases in crops are predicted using Multiscale Adaptive CNN with LSTM layer (MA-CNN-LSTM), where the parameters in MA-CNN-LSTM are optimized using Advanced Mountaineering Team-Based Optimization Algorithm (AMTBO). In the second phase, the input images are given to MA-CNN-LSTM to detect crop pests. Here, the AMTBO is utilized for tuning parameters. In the third phase, the soil quality and environment data are fed into the Multi-scale Adaptive 1DCNN with LSTM layer (MA-1D CNN-LSTM) to predict the smart irrigation, where the parameter optimization is done using the AMTBO. Smart irrigation enhances the growth of crops and minimizes water usage. In the final phase, the input data such as crop quality, soil quality, and environment data are given to the MA-1D CNN-LSTM to check the overall yield prediction in an agricultural region. Here, the parameters in MA-1D CNN-LSTM are optimized via the AMTBO. The simulation results are compared with other algorithms and classification techniques to check the performance of the developed model.

Most natural disasters result from geodynamic events such as landslides and slope collapse. These failures cause catastrophes that directly impact the environment and cause financial and human losses. Visual inspection is the primary method for detecting failures in geotechnical structures, but on-site visits can be risky due to unstable soil. In addition, the body design and hostile and remote installation conditions make monitoring these structures inviable. When a fast and secure evaluation is required, analysis by computational methods becomes feasible. In this study, a convolutional neural network (CNN) approach to computer vision is applied to identify defects in the surface of geotechnical structures aided by unmanned aerial vehicle (UAV) and mobile devices, aiming to reduce the reliance on human-led on-site inspections. However, studies in computer vision algorithms still need to be explored in this field due to particularities of geotechnical engineering, such as limited public datasets and redundant images. Thus, this study obtained images of surface failure indicators from slopes near a Brazilian national road, assisted by UAV and mobile devices. We then proposed a custom CNN and low complexity model architecture to build a binary classifier image-aided to detect faults in geotechnical surfaces. The model achieved a satisfactory average accuracy rate of 94.26%. An AUC metric score of 0.99 from the receiver operator characteristic (ROC) curve and matrix confusion with a testing dataset show satisfactory results. The results suggest that the capability of the model to distinguish between the classes 'damage' and 'intact' is excellent. It enables the identification of failure indicators. Early failure indicator detection on the surface of slopes can facilitate proper maintenance and alarms and prevent disasters, as the integrity of the soil directly affects the structures built around and above it.

Geomechanical assessment using coupled reservoir-geomechanical simulation is becoming increasingly important for analyzing the potential geomechanical risks in subsurface geological developments. However, a robust and efficient geomechanical upscaling technique for heterogeneous geological reservoirs is lacking to advance the applications of three-dimensional (3D) reservoir-scale geomechanical simulation considering detailed geological heterogeneities. Here, we develop convolutional neural network (CNN) proxies that reproduce the anisotropic nonlinear geomechanical response caused by lithological heterogeneity, and compute upscaled geomechanical properties from CNN proxies. The CNN proxies are trained using a large dataset of randomly generated spatially correlated sand-shale realizations as inputs and simulation results of their macroscopic geomechanical response as outputs. The trained CNN models can provide the upscaled shear strength (R-2 > 0.949), stress-strain behavior (R-2 > 0.925), and volumetric strain changes (R-2 > 0.958) that highly agree with the numerical simulation results while saving over two orders of magnitude of computational time. This is a major advantage in computing the upscaled geomechanical properties directly from geological realizations without the need to perform local numerical simulations to obtain the geomechanical response. The proposed CNN proxy-based upscaling technique has the ability to (1) bridge the gap between the fine-scale geocellular models considering geological uncertainties and computationally efficient geomechanical models used to assess the geomechanical risks of large-scale subsurface development, and (2) improve the efficiency of numerical upscaling techniques that rely on local numerical simulations, leading to significantly increased computational time for uncertainty quantification using numerous geological realizations. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geoenvironmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification.

In recent years, the rapid development of the world's economy has led to the large-scale development and utilization of ecological resources on the earth, due to which the ecological environment has been continuously and seriously damaged, resulting in the waste of resources, soil erosion, land desertification, etc. To avoid further damage to the ecological environment and ecological resources, improve the utilization rate of ecological resources, and ensure the sustainable development of human society, it is necessary to evaluate the ecological environment. In this study, we collected the required data using the Delphi method and remote sensing technology. Secondly, the green Olympic building evaluation system (which refers to the CASBEE method in Japan) was used to evaluate the impact of green roofs on architectural design and the urban ecological environment. Third, a deep learning (DL)-based hybrid model, which consists of a convolutional neural network (CNN) and long-short-term memory (SLSTM), known as CNN-LSTM, was used to evaluate the impact of green roofs on urban ecology and building architectural design. The influence of thermal comfort on the indoor environment of green roof buildings was studied. For experimentation, six samples of Shanghai Thumb Plaza, Splendid Tesco Point, Chaoshan Yuan Hotel, Green Management Office, Huangpu District Domestic Waste Transfer Station, and Changning District Fuxin Slaughterhouse were selected as evaluation objects, and the effect of green roofs on building design and urban ecology was evaluated from six levels: ecological, ornamental, safety, functional, social, and economic. Both the CASBEE and DL-based methods, CNN-LSTM, performed well and increased the evaluation results to some extent. The CNN-LSTM model increased the accuracy of the system by 3.55%, precision by 3.50%, recall by 4.46%, and F1-score by 3.30%. Overall, this study summarizes the existing problems of green rooftop buildings in Shanghai at this stage, which is conducive to formulating optimization strategies to improve the ecological benefits of green roof buildings and has important practical significance for realizing the sustainable development of human society.

Forests are essential to our planet's well-being, playing a vital role in climate regulation, biodiversity preservation, and soil protection, thus serving as a cornerstone of our global ecosystem. The threat posed by forest fires highlights the critical need for early detection systems, which are indispensable tools in safeguarding ecosystems, livelihoods, and communities from devastating destruction. In combating forest fires, a range of techniques is employed for efficient early detection. Notably, the combination of drones with artificial intelligence, particularly deep learning, holds significant promise in this regard. Image segmentation emerges as a versatile method, involving the partitioning of images into multiple segments to simplify representation, and it leverages deep learning for fire detection, continuous monitoring of high-risk areas, and precise damage assessment. This study provides a comprehensive examination of recent advancements in semantic segmentation based on deep learning, with a specific focus on Mask R-CNN (Mask Region Convolutional Neural Network) and YOLO (You Only Look Once) v5, v7, and v8 variants. The emphasis is placed on their relevance in forest fire monitoring, utilizing drones equipped with high-resolution cameras.

This study presents a deep learning model created for enabling comprehensive wildfire control by seamlessly combining satellite images, weather data and terrain details. Current systems face challenges in comprehensively analyzing these factors due to limitations in data integration, dynamic fire behavior prediction, and post-fire ecological impact evaluation. By improving detection and accurate assessment of impact, the system addresses all aspects of wildfire management from forecasting to post event analysis. The model integrates soil quality examination and vegetation regrowth simulation Using image analysis and state of the art deep learning methods. This holistic approach of Image analysis employs Convolutional Neural Networks (CNN) for predicting wildfire risk and Recurrent Neural Networks (RNN) for assessing soil and hydrological effects. This adaptable approach, which aims to transform the way fire control is done, can be readily adjusted to changing conditions and takes correlations between different aspects into account. It surpasses conventional techniques by including soil quality analysis, vegetation regrowth modeling, and vegetation damage evaluation. The adaptable nature of this method proves invaluable, in lessening the impact of wildfires with a focus, on evaluating vegetation damage and promoting restoration.

黄河源区是黄河流域的主要产水区和水源涵养区，积雪融水是源区的重要水源之一，高精度积雪面积数据集是源区生态水文模拟、气候变化等研究的基础。MODIS积雪产品是最广泛使用的积雪面积数据集之一。然而，MODIS积雪产品中大量的云覆盖，导致了近乎一半的信息缺失。黄河源区季节性积雪多呈现出雪层偏浅、斑块状分布且消融快等特点，使得传统统计方法很难准确捕获源区的积雪时空特征，而先进的深度学习技术能更好地深入挖掘隐藏在数据背后的时空特征。本研究利用2000–2021年逐日500 m空间分辨率的MODIS归一化积雪指数（NDSI）产品，使用基于部分卷积神经网络（PCNN）的MODIS NDSI云像元重建模型，在生成时空连续MODIS NDSI数据集的基础上，进一步采用NASA原始积雪覆盖比例（FSC）产品的标准算法，制备黄河源区2000–2021年逐日、0.005°(约500 m)的无云MODIS FSC数据集。基于源区6个地面气象台站雪深观测资料和“云假设”两方面的验证表明，数据集的总体精度可以达到94%，高估和低估均为1%，平均绝对偏差10.43%，平均相关系数为0.93，表明数据具有较高精度，与晴...