Despite their importance to ecosystem services, wetlands are threatened by pollution and development. Over the last few decades, a growing number of wetland studies employed remote sensing (RS) to scientifically monitor the status of wetlands and support their sustainability. Considering the rapid evolution of wetland studies and significant progress that has been made in the field, this paper constitutes an overview of studies utilizing RS methods in wetland monitoring. It investigates publications from 1990 up to the middle of 2022, providing a systematic survey on RS data type, machine learning (ML) tools, publication details (e.g., authors, affiliations, citations, and publications date), case studies, accuracy metrics, and other parameters of interest for RS-based wetland studies by covering 344 papers. The RS data and ML combination is deemed helpful for wetland monitoring and multi-proxy studies, and it may open up new perspectives for research studies. In a rapidly changing wetlands landscape, integrating multiple RS data types and ML algorithms is an opportunity to advance science support for management decisions. This paper provides insight into the selection of suitable ML and RS data types for the detailed monitoring of wetland-associated systems. The synthesized findings of this paper are essential to determining best practices for environmental management, restoration, and conservation of wetlands. This meta-analysis establishes avenues for future research and outlines a baseline framework to facilitate further scientific research using the latest state-of-art ML tools for processing RS data. Overall, the present work recommends that wetland sustainability requires a special land-use policy and relevant protocols, regulation, and/or legislation.

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Remote Sensing and Machine Learning Tools to Support Wetland Monitoring: A Meta-Analysis of Three Decades of Research


 This paper presents a low-cost, accurate indoor positioning system that integrates image acquisition and processing and data-driven modeling algorithms for robotics research and education. Multiple overhead cameras are used to obtain normalized image coordinates of ArUco markers, and a new procedure is developed to convert them to the camera coordinate frame. Various data-driven models are proposed to establish a mapping relationship between the camera and the world coordinates. One hundred fifty data pairs in the camera and world coordinates are generated by measuring the ArUco marker at different locations and then used to train and test the data-driven models. With the model, the world coordinate values of the ArUco marker and its robot carrier can be determined in real time. Through comparison, it is found that a straightforward polynomial regression outperforms the other methods and achieves a positioning accuracy of about 1.5 cm. Experiments are also carried out to evaluate its feasibility for use in robot control. The developed system (both hardware and algorithms) is shared as an open source and is anticipated to contribute to robotic studies and education in resource-limited environments and underdeveloped regions.

A low-cost indoor positioning system based on data-driven modeling for robotics research and education

Accurate indoor localization estimation has important social and commercial values, such as indoor location services and pedestrian retention times. Acoustic-based methods can achieve high localization accuracies in specific scenarios with special equipment; however, it is a challenge to obtain accurate localization with general equipment in indoor environments. To solve this problem, we propose a novel fusion CHAN and the improved pedestrian dead reckoning (PDR) indoor localization system (CHAN-IPDR-ILS). In this system, we propose a step length estimation method that adds the previous two steps for extracting more accurate information to estimate the current step length. The maximum influence factor is set for the previous two steps to ensure the importance of the current step length. We also propose a heading direction correction method to mitigate the errors in sensor data. Finally, pedestrian localization is achieved using a motion model with acoustic estimation and dynamic improved PDR estimation. In the fusion localization, the threshold and confidence level of the distance between estimation base-acoustic and improved PDR estimation are set to mitigate accidental and cumulative errors. The experiments were performed at trial sites with different users, devices, and scenarios, and experimental results demonstrate that the proposed method can achieve a higher accuracy compared with the state-of-the-art methods. The proposed fusion localization system manages equipment heterogeneity and provides generality and flexibility with different devices and scenarios at a low cost.

/pdf/multi-information-fusion-indoor-localization-using-12kb4lis.pdf

Multi-Information Fusion Indoor Localization Using Smartphones

Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.

https://www.mdpi.com/1424-8220/22/15/5505/pdf?version=1659409735

A Low-Cost and Efficient Indoor Fusion Localization Method

In this paper, GF-1 WVF image and Sentinel-1 SAR image covering Huixian wetland area are used as data sources. The Gram Schmidt (GS) algorithm is first used to fuse GF-1 images and SAR images with different polarization modes, and then the Random Forest (RF) algorithm is used for supervised classification. Finally, the accuracy of classification results and the ability to extract information are compared. The experimental results show that the fusion image has obvious texture features and prominent karst landform features, compared with the GF-1 WVF image. Compared with the Sentinel-1 SAR image, the fusion image has obvious spectral features. Spectral differences between typical features are large; The overall classification accuracy of GF-1 images, GF-1 and Sentinel-1 VV polarization fusion images, and GF-1 and Sentinel-1 VH polarization fusion images have reached over 80%. The classification accuracy of GF-1 and Sentinel-1 VV polarization fusion images reaches 85.15%, which is better than GF-1 and Sentinel-1 VH polarization fusion images. The classification accuracy of water bodies in the VV polarization fusion image is better than that of GF-1. Bare ground has the highest classification accuracy among all fused images.

Land Cover Classification of Huixian Wetland Based on SAR and Optical Image Fusion

Location Based Service (LBS) is becoming increasingly valuable in many indoor application scenarios. However, existing indoor positioning technologies are either too costly or have high computational volumes. It is a challenge to achieve positioning both at low cost and low computation. To address this problem, we propose an indoor positioning algorithm based on computer vision, which can achieve localization at a low-cost and low computational complexity. In the proposed method, the lightweight MobileNet network is used to build an image classification model to train and optimize the collected dataset, then the predicted positions are obtained by inputting the images of the locations to be measured into the trained model. The experimental results demonstrate that the MobileNet model has lower computational effort than the classical convolutional neural network CNN. The designed computer vision-based method can successfully recognize images for indoor positioning, which is highly practical. ∗Corresponding author Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. APIT 2022, January 14–16, 2022, Virtual Event, Thailand © 2022 Association for Computing Machinery. ACM ISBN 978-1-4503-9557-1/22/01. . . $15.00 https://doi.org/10.1145/3512353.3512378 CCS CONCEPTS •Computingmethodologies→Artificial intelligence; Computer vision.

Low-cost and lightweight indoor positioning based on computer vision

In recent years, natural disasters are frequent, especially landslides, which are always threatening people’s lives and properties. It is of great significance to establish a deformation monitoring system using modern science and technology to realize real-time monitoring of landslides for people’s disaster prevention and avoidance. This paper addresses the problems of the existing deformation monitoring system such as outdated architecture, not easy to upgrade and optimize, long system data link, and long data query time, and researches the deformation monitoring algorithm with multi-source sensors, and designs the deformation monitoring system based on microservice architecture, and completes the development of the deformation system to provide more timely, reliable and stable deformation monitoring service.

Jianming Xiao

Papers

A Low-Cost and Efficient Indoor Fusion Localization Method

Land Cover Classification of Huixian Wetland Based on SAR and Optical Image Fusion

Low-cost and lightweight indoor positioning based on computer vision

Architecture Design of Deformation Monitoring System Based on Microservice Architecture

Multi-Information Fusion Indoor Localization Using Smartphones