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This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

We make use of the Volunteered Geographic Information (VGI) data to extract the total extent of the roads using remote sensing images. VGI data is often provided only as vector data represented by lines and not as full extent. Also, high geolocation accuracy is not guaranteed and it is common to observe misalignment with the target road segments by several pixels on the images. In this work, we use the prior information provided by the VGI and extract the full road extent even if there is significant mis-registration between the VGI and the image. The method consists of image segmentation and traversal of multiple agents along available VGI information. First, we perform image segmentation, and then we traverse through the fragmented road segments using autonomous agents to obtain a complete road map in a semi-automatic way once the seed-points are defined. The road center-line in the VGI guides the process and allows us to discover and extract the full extent of the road network based on the image data. The results demonstrate the validity and good performance of the proposed method for road extraction that reflects the actual road width despite the presence of disturbances such as shadows, cars and trees which shows the efficiency of the fusion of the VGI and satellite images.

Remotely piloted aircraft (RPA) systems have emerged as an established tool within the construction industry. Concurrent with this trend has been the rise in research on RPA, establishing this as a new field of study within the construction management domain. What is needed now is an assessment of the current state of research in this emerging discipline – its strengths and weaknesses – by which future research on RPA in construction may be guided. The purpose of this paper is to address this need.

A total of 59 peer-reviewed journal articles covering RPAs within the construction domain were systematically reviewed using a mixed-methods approach, utilizing qualitative-scientometric analyses techniques.

The results reveal a field of study in its fledgling stage, with a limited number of experts operating somewhat in isolation, from a limited number of institutions. Key publication outlets are identified, with the main focus of research being in the technical areas of remote sensing, photogrammetry and image processing.

The study benefits researchers and industry practitioners alike. For researchers, the identified gaps reveal areas of high priority in future research. For construction companies, particularly small to medium-sized businesses, the study raises awareness of the latest developments and potential applicability of RPAs in the industry.

The study exposes what is missing from current research: a broader consideration of organizational adjustments needed to accommodate RPA usage, economic analyses and impediments to wider acceptance.

This paper aims to develope an integrated image processing method to investigate the spatiotemporal dynamics of Phragmites invasion in the Detroit River International Wildlife Refuge on the basis of publically available sources.

This new approach integrates the standard time-series analysis of Landsat images with USDA National Agriculture Imagery Program (NAIP) imagery and USGS Digital Orthophoto Quarter Quads (DOQQ) datasets, which are either classified or manually interpreted with the aid of ground control points. Three different types of spatiotemporal dimensions are designed to test this integrated time-series image analysis method: the selected sites and selected time-points with high spatial resolution and sufficient validation data points, the intermediate time-series with continued yearly images and periodical validation data, and the long time-series with periodical images without enough validation data. The support vector machine (SVM) method was used to classify the Landast TM sequence images to detect the Phragmites invasion.

The habitat map produced by NAIP images and field collection data shows that the total Phragmites area of DRIWR in 2010 is 4221.87 acres without treatment areas and similar with the removed non-vegetation method. It is confirmed that the pre-classification method can obtain more accurate results.

The test results show that the Landsat-5 data can be used for long-term environmental management and monitoring of Phragmites invasion and can achieve rehabilitation of invasion areas.

Knowledge discovery in databases aims to discover useful and significant information from multiple databases. However, in the remote sensing field, the large size of discovered information makes it hard to manually look for interesting information quickly and easily. The purpose of this paper is to automate the process of identifying interesting spatiotemporal knowledge (expressed as rules).

The proposed approach is based on case‐based reasoning (CBR) process. CBR allows the recognition of useful and interesting rules by simulating a human reasoning process, and combining objective and subjective interestingness measures. It takes advantage of statistics' power from objective criteria and the reliability of subjective criteria. This helps improve the discovery of interesting rules by taking into consideration the different properties of interestingness measures.

The proposed approach combines several interestingness measures with complementary properties to improve the detection of the interesting rules. Based on a CBR process, it, also, offers three main advantages to users in a remote sensing field: automatism, integration of the users' expectations and combination of several interestingness measures while taking into account the reliability of each one. The performance of the proposed approach is evaluated and compared to other approaches using several real‐world datasets.

This study reports a valuable decision support tool for engineers, environmental authority and personnel who want to identify relevant discovered rules. The resulting rules are useful for many fields such as: disaster prevention and monitoring, growth volume and crops on farm or grassland, planting status of agricultural products, and tree distribution of forests.

In the past three decades, remote sensing-based models for estimating crop yield have addressed critical problems of general food security, as the unavailability of grains such as rice creates serious worldwide food insecurity problems. The main purpose of this study was to compare the potential of time-series Landsat-8 and Sentinel-2 data to predict rice yield several weeks before harvest on a regional scale.

To this end, the sum of normalized difference vegetation index (NDVI)-based models created the best agreement with actual yield data at the golden time window of six weeks before harvest when rice grains were in milky and mature growth stages. The application of nine other vegetation indicators was also investigated in the golden time window in comparison to NDVI.

The findings of this study demonstrate the viability of identifying locations with poor and superior performance in terms of production management approaches through a rapid and economical solution for early rice grain yield assessment. Results indicated that while some of those, such as enhanced vegetation index (EVI) and optimized soil adjusted vegetation index, were able to estimate rice yield with high accuracy, NDVI is still the best indicator to predict rice yield before harvest. However, experiments can be conducted in different regions in future studies to evaluate the generalizability of the approach.

To achieve this objective, the authors considered the following purposes: using Sentinel-2 time-series data, determining the appropriate growth stage for estimating rice yield and evaluating different vegetation indices for estimating rice yield.

The purpose of this paper is to present a method and results of evaluating damaged building extraction using an object recognition task in pre‐ and post‐tsunami event. The advantage of remote sensing and its applications made it possible to extract damaged building images and vulnerability easement of wide urban areas due to natural disasters.

The proposed approach involves several advanced morphological operators, among which are adaptive transforms with varying size, shape and grey level of the structuring elements. IKONOS‐2 satellite images consisting of pre‐ and post‐2004 Indian Ocean Tsunami site of the Kalmunai area on the East coast of Sri Lanka were used. Morphological operation using structural element are applied for segmented images, then extracted remaining building foot print using random forest classification method. This work extended further the road lines extraction using Hough transform.

The result was investigated using geographic information system (GIS) data and global positioning system (GPS) ground survey in the field and it appeared to have high accuracy: the confidence measures produced of a completely destroyed structure give 86 percent by object‐based, respectively, after the tsunami in one segment of Maruthamune GN Division.

This study has also identified significant limitations, due to the resolution and clearness of satellite images and vegetation canopy over the building footprint.

The authors develop an automated method to detect damaged buildings and compare the results with GIS‐based ground survey.

This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed.

First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions.

Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future.

This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.

This paper aims to demonstrate the importance of taking into account precipitation and the vegetation response to it when trying to analyse changes of vegetation cover in drylands with high inter‐annual rainfall variability.

Linear regression models were used to determine trends in NDVI and precipitation and their interrelations for each pixel. Trends in NDVI that were entirely supported by precipitation trends were considered to impose climate‐induced vegetation change. Trends in NDVI that were not explained by trends in precipitation were considered to mark human‐induced vegetation change. Modelling results were validated by test of statistical significance and by comparison with the data from higher resolution satellites and fieldtrips to key test sites.

More than 26 percent of all vegetated area in Central Asia experienced significant changes during 1981‐2000. Rainfall has been proved to enforce most of these changes (21 percent of the entire vegetated area). The trends in vegetation activity driven by anthropogenic factor are much scarcer and occupy about 5.75 percent of the studied area.

Planners, decision makers and other interest groups can use the findings of the study for assessment and monitoring land performance/land degradation over dry regions.

The study demonstrates the importance of taking into account precipitation and the vegetation response to it when trying to analyse changes of vegetation cover in drylands with high inter‐annual rainfall variability.