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With the enhancement of location-acquisition technologies, GPS trajectories play an essential role in data-driven intelligent transportation applications, which requires an accurate approach to match raw GPS trajectories to road segments on a digital map. However, for complex urban roads containing elevated roads and surface roads, map matching for low-frequency GPS data is still challenging. This paper aims to address the biases and instability problem in existing approaches. To this end, we combine the spatial-temporal characteristics of GPS data in complex roads with driving behaviours and present a novel global map matching method including truncated density clustering algorithm, statistic features based spatial-temporal analysis, and driving-behaviour-based track modification. Additionally, a weighted-matrix based interactive voting algorithm is proposed to select the best results from a global perspective. The experiments are conducted with two real GPS trajectory datasets under three road conditions. The result shows that our approach outperforms state-of-art approaches for urban complex road networks in both accuracy and efficiency.

Combining multiple LiDARs enables a robot to maximize its perceptual awareness of environments and obtain sufficient measurements, which is promising for simultaneous localization and mapping (SLAM). This paper proposes a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for multiple LiDARs. Our approach starts with measurement preprocessing to extract edge and planar features from raw measurements. After a motion and extrinsic initialization procedure, a sliding window-based multi-LiDAR odometry runs onboard to estimate poses with online calibration refinement and convergence identification. We further develop a mapping algorithm to construct a global map and optimize poses with sufficient features together with a method to model and reduce data uncertainty. We validate our approach's performance with extensive experiments on ten sequences (4.60km total length) for the calibration and SLAM and compare them against the state-of-the-art. We demonstrate that the proposed work is a complete, robust, and extensible system for various multi-LiDAR setups. The source code, datasets, and demonstrations are available at https://ram-lab.com/file/site/m-loam., 20 pages, 22 figures, accepted by IEEE Transcation on Robotics

Methods of rainstorm disaster risk monitoring based on retrieved satellite rainfall data are studied. Due to significant regional differences in global rainstorm disasters, they are not only affected by geographical location, topography, and rivers, but also by climate events. It is necessary to study disaster-causing factors, hazard-formative environments, and hazard-affected incidents of rainstorm disasters based on the climate distribution characteristics of precipitation and rainstorm disasters worldwide. According to a global flood disaster data-set from the last 20 years, the top four flood disaster causes (accounting for 96.8% in total) related to precipitation, from most to least influential, are heavy rain (accounting for 61.6%), brief torrential rain (16.7%), monsoonal rain (9.4%), and tropical cyclone/storm rain (9.1%). The dynamic global rainstorm disaster threshold is established using global climate data on 3319 rainstorm-induced floods and rainfall retrieved by satellites in the last 20 years. Taking the 7-day accumulated rainfall, 3-hour and 12-hour maximum rainfall, 24-hour rainfall, rainstorm threshold, and others as the main parameters, a rainstorm intensity index is established. The calculation method and global map of hazard-formative environmental factor and hazard-affected body factor of rainstorm disasters are formed based on the terrain, river datasets, the population, and economic datasets. Finally, a satellite remote sensing rainstorm disaster risk monitoring model is established including these three factors (rainstorm intensity index, hazard-formative environment factor, and hazard-affected body factor). The results show that the model can well describe the rainstorm disasters that happened in the middle and lower reaches of the Yangtze River in China and in South Asia in 2020.

Consistent information on the current elemental composition of vegetation at global scale and the variables that determine it is lacking. To fill this gap, we gathered a total of 30 912 georeferenced records on woody plants foliar concentrations of nitrogen (N), phosphorus (P) and potassium (K) from published databases, and produced global maps of foliar N, P and K concentrations for woody plants using neural networks at a resolution of 1 km(2). We used data for climate, atmospheric deposition, soil and morphoclimatic groups to train the neural networks. Foliar N, P and K do not follow clear global latitudinal patterns but are consistent with the hypothesis of soil substrate age. We additionally built generalized linear mixed models to investigate the evolutionary history effect together with the effects of environmental effects. In this comparison, evolutionary history effects explained most of the variability in all cases (mostly > 60%). These results emphasize the determinant role of evolutionary history in foliar elemental composition, which should be incorporated in upcoming dynamic global vegetation models.

For studying the flow of glaciers and their response to climate change it is important to detect glacier surges. Here, we compute within Google Earth Engine the normalized differences between winter maxima of Sentinel-1 C-band radar backscatter image stacks over subsequent years. We arrive at a global map of annual backscatter changes, which are for glaciers in most cases related to changed crevassing associated with surge-type activity. For our demonstration period 2018–2019 we detected 69 surging glaciers, with many of them not classified so far as surge type. Comparison with glacier surface velocities shows that we reliably find known surge activities. Our method can support operational monitoring of glacier surges, and some other special events such as large rock and snow avalanches.

Most real-time autonomous robot applications require a robot to traverse through a dynamic space for a long time. In some cases, a robot needs to work in the same environment. Such applications give rise to the problem of a life-long SLAM system. Life-long SLAM presents two main challenges i.e. the tracking should not fail in a dynamic environment and the need for a robust and efficient mapping strategy. The system should update maps with new information; while also keeping track of older observations. But, mapping for a long time can require higher computational requirements. In this paper, we propose a solution to the problem of life-long SLAM. We represent the global map as a set of rasterized images of local maps along with a map management system responsible for updating local maps and keeping track of older values. We also present an efficient approach of using the bag of visual words method for loop closure detection and relocalization. We evaluate the performance of our system on the KITTI dataset and an indoor dataset. Our loop closure system reported recall and precision of above 90 percent. The computational cost of our system is much lower as compared to state-of-the-art methods. Our method reports lower computational requirements even for long-term operation.

Eutrophication is an emerging global issue associated with increasing anthropogenic nutrient loading. The impacts and extent of eutrophication are often limited to regions with dedicated monitoring programmes. Here we introduce the first global and Google Earth Engine-based interactive assessment tool of coastal eutrophication potential (CEP). The tool evaluates trends in satellite-derived chlorophyll-a (CHL) to devise a global map of CEP. Our analyses suggest that, globally, coastal waters (depth ≤200 m) covering ∼1.15 million km2 are eutrophic potential. Also, waters associated with CHL increasing trends—eutrophication potential—are twofold higher than those showing signs of recovery. The tool effectively identified areas of known eutrophication with severe symptoms, like dead zones, as well as those with limited to no information of the eutrophication. Our tool introduces the prospect for a consistent global assessment of eutrophication trends with major implications for monitoring Sustainable Development Goals (SDGs) and the application of Earth Observations in support of SDGs., Satellite-derived chlorophyll data and Google Earth Engine (GEE) are used to introduce the first global map of coastal eutrophication potential as a GEE app. The prospects of the app being used as a global framework for eutrophication screening/monitoring are discussed.

Multirobot simultaneous localization and mapping (SLAM) requires technical ingredients such as systematic construction of multiple SLAM systems and collection of information from each robot. In particular, map fusion is an essential process of multirobot SLAM that combines multiple local maps estimated by team robots into a global map. Fusion of multiple local maps is usually based on interloop detection that recognizes the same scene visited by multiple robots, or robot rendezvous where a member(s) of a robot team is observed in another member's images. This article proposes a collaborative monocular SLAM including a map fusion algorithm that utilizes rendezvous, which can happen when multirobot team members operate in close proximity. Unlike existing rendezvous-based approaches that require additional sensors, the proposed system uses a monocular camera only. Our system can recognize robot rendezvous using nonstatic features (NSFs) without fiducial markers to identify team robots. NSFs, which are abandoned as outliers in typical SLAM systems for not supporting ego-motion, can include relative bearing measurements between robots in a rendezvous situation. The proposed pipeline consists of the following: first, a feature identification module that extracts the relative bearing measurements between robots from NSFs consisting of anonymous bearing vectors with false positives, and second, a map fusion module that integrates the map from the observer robot with the maps from the observed robots using identified relative measurements. The feature identification module can operate quickly using the proposed alternating minimization algorithm formulated by two subproblems with closed-form solutions. The experimental results confirm that our collaborative monocular SLAM system recognizes rendezvous rapidly and robustly, and fuses local maps of team robots into a global map accurately.

We propose a probabilistic framework for multi-modal global localisation using 3D point correspondences without needing to integrate over SE(3) for Bayesian inference. A finite set of transformation candidates is constructed by decomposing the known global map into local places and computing the maximum likelihood transformation at each place using place specific 3D correspondences. An acceptance region around the maximum a posteriori candidate is then used to calculate the certainty of the location estimate. The 3D correspondences used consist of 3D positions estimated by a LiDAR and horizon points observed by cameras. Our empirical results show that visual correspondences can increase the certainty of the estimated location and improve localisation performance when far away from the trajectory used to construct the known global map. We analyse situations where improved rotation estimation of the transformation candidates reduces the certainty of the localisation. We also highlight the efficacy of the certainty as a measure of success and show that the framework's success rate is increased by $\text{12}\%$ when using the certainty as a termination criterion compared to a state-of-the-art LiDAR intensity benchmark (Guo, 2019).

Obstacle avoidance and navigation (OAN) algorithms typically employ offline or online methods. The former is fast but requires knowledge of a global map, while the latter is usually more computationally heavy in explicit solution methods, or is lacking in configurability in the form of artificial intelligence (AI) enabled agents. In order for OAN algorithms to be brought to mass produced robots, more specifically for multirotor unmanned aerial vehicles (UAVs), the computational requirement of these algorithms must be brought low enough such that its computation can be done entirely onboard a companion computer, while being flexible enough to function without a prior map, as is the case of most real life scenarios. In this paper, a highly configurable algorithm, dubbed Closest Obstacle Avoidance and A* (COAA*), that is lightweight enough to run on the companion computer of the UAV is proposed. This algorithm frees up from the conventional drawbacks of offline and online OAN algorithms, while having guaranteed convergence to a global minimum. The algorithms have been successfully implemented on the Heavy Lift Experimental (HLX) UAV of the Autonomous Robots Research Cluster in Taylor’s University, and the simulated results match the real results sufficiently to show that the algorithm has potential for widespread implementation.

Existing studies on indoor navigation often require such a pre-deployment as floor map, localization system and/or additional (customized) hardwares, or human motion traces, making them prohibitive when the situation deviates from these requirements (e.g., navigating a crowd of panicking people where no localization system or motion traces are available). The main observation inspiring our work without reliance on such pre-deployment is that when there are sufficient participants (e.g., a crowd of panicking people), the WiFi signatures collected by participants can serve as the fingerprints (referred to as location fingerprints ) of their unknown locations. By computing relative positions of these location fingerprints we can connect them to form a global map. Such a map reflects the topology of the underlying walkable space and thus holds the potential of offering a navigation path for any intended users. Based on this observation, we design Fly-Navi, a crowdsourcing based indoor navigation system via on-the-fly map generation, and primarily designed for indoor environments with rectilinear and narrow corridors. Specifically, each participant uploads sensory data, and the server then generates a global map (on-the-fly map) through a series of operations such as local map generation, local map stitch and edge computation. On top of the global map, Fly-Navi computes a navigation path to the given destination and tracks the progress. We implement the prototype of Fly-Navi and our experiments show that Fly-Navi can quickly generate a correct global map with the 80-percentile of between-fingerprint distance error less than 3 meters, which is important for computing turning points of the map and hereon offering turn-by-turn instructions, and correctly navigate the intended users to their destinations.

This paper presents an analysis to determine the suitable areas for the implementation of solar-wind projects in Santa Marta, Colombia. For this, an approach that integrates the decision-making tool of the Analytical Hierarchical Process (AHP) with Geographic Information Systems (GIS) was used. To identify the selection criteria, the existing literature and the renewable resources available in the study area were considered. Seven sub-criteria were identified (solar irradiation, wind speed, protected areas, armed conflict, indigenous reservations, populated zones and roads), which were weighted with the AHP by 15 experts with experience in projects with renewable energies. After performing a consistency analysis, it was found that 5 experts were consistent, while another 5 were discarded for being highly inconsistent. A consistency correction method was implemented for the remaining 5 experts, in order to have 10 experts for the weighting process. Each sub-criterion represents a map of the study area, which were superimposed in a GIS environment to implement the weighting obtained with the experts. A global map for the implementation of renewable energy projects, considering environmental, social and local infrastructure criteria was obtained. The map was classified into five categories: not suitable, marginally suitable, moderately suitable, highly suitable and optimally areas.Keywords: Renewable Energy, Analytical Hierarchical Process, Energy Planning, Geographic Information SystemsJEL Classifications: Q20, D70, Q42, D81DOI: https://doi.org/10.32479/ijeep.11266

Summary Sand, gravel, and crushed stone are the most mined materials on Earth. Aggregates constitute the foundation for modern civilization and are essential for providing shelter, infrastructure, and communication, but are an increasingly scarce resource. Here, we review the interconnections between the impacts of aggregate mining and the services they provide. We show that the conflicting impacts on the environment and humankind disrupt the net positive effects of aggregate mining on sustainable development. Focusing on low- and middle-income countries, we link these interconnections to the United Nations Sustainable Development Goals and identify critical obstacles to a sustainable future for global aggregate resources. Our assessment identifies an urgent need to improve knowledge on: (1) direct and indirect impacts of extraction on human health, (2) system-level impacts on ecosystems and the services they provide, and (3) how to meet the projected trajectories of global aggregate demand.

In this article, a novel dense underwater 3-D mapping paradigm based on pose graph simultaneous localization and mapping (SLAM) using an acoustic camera mounted on a rotator is proposed. The demands of underwater tasks, such as unmanned construction using robots, are growing rapidly. In recent years, the acoustic camera, which is a state-of-the-art forward-looking imaging sonar, has been gradually applied in underwater exploration. However, distinctive imaging principles make it difficult to gain an intuitive perception of an underwater environment. In this study, an acoustic camera with a rotator was used for dense 3-D mapping of the underwater environment. The proposed method first applies a 3-D occupancy mapping framework based on the acoustic camera rotating around the acoustic axis using a rotator at a stationary position to generate 3-D local maps. Then, scan matching of adjacent local maps is implemented to calculate odometry without involving internal sensors, and an approximate dense global map is built in real time. Finally, based on a graph optimization scheme, offline refinement is performed to generate a final dense global map. Our experimental results demonstrate that our 3-D mapping framework for an acoustic camera can achieve dense 3-D mapping of underwater environments robustly and accurately.

For a multi-robot system, the accurate global map building based on a local map obtained by a single robot is an essential issue. The map building process is always divided into three stages: single-robot map acquisition, multi-robot map transmission, and multi-robot map merging. Based on the different stages of map building, this paper proposes a multi-staqe optimization (MSO) method to improve the accuracy of the global map. In the map acquisition stage, we windowed the map based on the position of the robot to obtain the local map. Furthermore, we adopted the extended Kalman filter (EKF) to improve the positioning accuracy, thereby enhancing the accuracy of the map acquisition by the single robot. In the map transmission stage, considering the robustness of the multi-robot system in the real environment, we designed a dynamic self-organized communication topology (DSCT) based on the master and slave sketch to ensure the efficiency and accuracy of map transferring. In the map merging stage, multi-layer information filtering (MLIF) was investigated to increase the accuracy of the global map. We performed simulation experiments on the Gazebo platform and compared the result of the proposed method with that of classic map building methods. In addition, the practicability of this method has been verified on the Turtlebot3 burger robot. Experimental results proved that the MSO method improves the accuracy of the global map built by the multi-robot system.

In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.

Oil seed crops, especially oil palm, are among the most rapidly expanding agricultural land uses, and their expansion is known to cause significant environmental damage. Accordingly, these crops often feature in public and policy debates which are hampered or biased by a lack of accurate information on environmental impacts. In particular, the lack of accurate global crop maps remains a concern. Recent advances in deep-learning and remotely sensed data access make it possible to address this gap. We present a map of closed-canopy oil palm (Elaeis guineensis) plantations by typology (industrial versus smallholder plantations) at the global scale and with unprecedented detail (10 m resolution) for the year 2019. The DeepLabv3+ model, a convolutional neural network (CNN) for semantic segmentation, was trained to classify Sentinel-1 and Sentinel-2 images onto an oil palm land cover map. The characteristic backscatter response of closed-canopy oil palm stands in Sentinel-1 and the ability of CNN to learn spatial patterns, such as the harvest road networks, allowed the distinction between industrial and smallholder plantations globally (overall accuracy =98.52±0.20 %), outperforming the accuracy of existing regional oil palm datasets that used conventional machine-learning algorithms. The user's accuracy, reflecting commission error, in industrial and smallholders was 88.22 ± 2.73 % and 76.56 ± 4.53 %, and the producer's accuracy, reflecting omission error, was 75.78 ± 3.55 % and 86.92 ± 5.12 %, respectively. The global oil palm layer reveals that closed-canopy oil palm plantations are found in 49 countries, covering a mapped area of 19.60 Mha; the area estimate was 21.00 ± 0.42 Mha (72.7 % industrial and 27.3 % smallholder plantations). Southeast Asia ranks as the main producing region with an oil palm area estimate of 18.69 ± 0.33 Mha or 89 % of global closed-canopy plantations. Our analysis confirms significant regional variation in the ratio of industrial versus smallholder growers, but it also confirms that, from a typical land development perspective, large areas of legally defined smallholder oil palm resemble industrial-scale plantings. Since our study identified only closed-canopy oil palm stands, our area estimate was lower than the harvested area reported by the Food and Agriculture Organization (FAO), particularly in West Africa, due to the omission of young and sparse oil palm stands, oil palm in nonhomogeneous settings, and semi-wild oil palm plantations. An accurate global map of planted oil palm can help to shape the ongoing debate about the environmental impacts of oil seed crop expansion, especially if other crops can be mapped to the same level of accuracy. As our model can be regularly rerun as new images become available, it can be used to monitor the expansion of the crop in monocultural settings. The global oil palm layer for the second half of 2019 at a spatial resolution of 10 m can be found at https://doi.org/10.5281/zenodo.4473715 (Descals et al., 2021).

In recent years, unmanned aerial vehicles (UAVs), especially swarming UAVs are widely deployed in a variety of Internet-of-Things (IoT) scenarios. Since UAVs’ positions are essential for their collaboration, high-precision localization for swarming UAVs has attracted a lot of attention. Although the global positioning system (GPS) receiver has been widely integrated in UAV, it is not accurate enough and is prone to accidental or deliberate interferences. In this article, we propose a distributed and collaborative localization method for swarming UAVs that combines super multidimensional scaling (SMDS) and patch dividing/merging with GPS information. Specifically, the SMDS is first used to get the relative coordinates of the UAVs in each patch, then we merge relative map patches into a global map and transform the relative coordinates of the UAVs to their absolute coordinates. Furthermore, we propose a low-complexity algorithm that greatly reduces the computational complexity of SMDS with a large number of UAVs. Simulation results validate that with accurate enough angle measurements, the proposed SMDS localization algorithm outperforms the other MDS-based collaborative localization algorithms and can greatly improve the localization accuracy and robustness of swarming UAVs.

This paper presents a robust underwater simultaneous localisation and mapping (SLAM) framework using autonomous relocalisation. The proposed approach strives to maintain a single consistent map during operation and updates its current plan when the SLAM loses feature tracking. The updated plan transverses viewpoints that are likely to aid in merging the current map into the global map. We present the sub-systems of the framework: the SLAM, viewpoint generation, and high level planning. In-water experiments show the advantage of our approach used on an autonomous underwater vehicle (AUV) performing inspections.

In this paper, we present a framework for performing collaborative localization for groups of micro aerial vehicles (MAV) that use vision based sensing. The vehicles are each assumed to be equipped with a forward-facing monocular camera, and to be capable of communicating with each other. This collaborative localization approach is developed as a decentralized algorithm and built in a distributed fashion where individual and relative pose estimation techniques are combined for the group to localize against surrounding environments. The MAVs initially detect and match salient features between each other to create a sparse reconstruction of the observed environment, which acts as a global map. Once a map is available, each MAV performs feature detection and tracking with a robust outlier rejection process to estimate its own pose in 6 degrees of freedom. Occasionally, one or more MAVs can be tasked to compute poses for another MAV through relative measurements, which is achieved by exploiting multiple view geometry concepts. These relative measurements are then fused with individual measurements in a consistent fashion. We present the results of the algorithm on image data from MAV flights both in simulation and real life, and discuss the advantages of collaborative localization in improving pose estimation accuracy., Supplementary video at https://www.youtube.com/watch?v=LvaTOWuTOPo

Mapping, as the back-end of perception and the front-end of path planning in the modern UAV navigation system, draws our interest. Considering the requirements of UAV navigation and the features of the current embedded computation platforms, we designed and implemented a novel multilayer mapping framework. In this framework, we divided the map into three layers: awareness, local, and global. The awareness map is constructed in cylindrical coordinate, enabling fast raycasting. The local map is a probability-based volumetric map. The global map adopts dynamic memory management, allocating memory for the active mapping area, and recycling the memory from the inactive mapping area. We implemented this mapping framework in three parallel threads: awareness thread, local-global thread, and visualization thread. Finally, we evaluated the mapping kit in both the simulation environment and the real-world scenario with the vision-based sensors. The framework supports different kinds of map outputs for the global or local path planners. The implementation is open-source for the research community.

This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method for wheeled mobile robots based on deep imitation learning. The proposed method is a data-driven control method that directly uses LiDAR sensors and relative target position for mobile robot navigation control. A deep convolutional neural network (CNN) model is proposed to predict motion control commands of the mobile robot without the requirement of the global map to achieve navigation control of the mobile robot in unknown environments. While collecting the training dataset, we manipulated the mobile robot to avoid obstacles through manual control and recorded the raw data of the LiDAR sensor, the relative target position, and the corresponding motion control commands. Next, we applied a data augmentation method on the recorded samples to increase the number of training samples in the dataset. In the network model design, the proposed CNN model consists of a LiDAR CNN module to extract LiDAR features and a motion prediction module to predict the motion behavior of the robot. In the model training phase, the proposed CNN model learns the mapping between the input sensor data and the desired motion behavior through end-to-end imitation learning. Experimental results show that the proposed mapless LiDAR navigation control method can safely navigate the mobile robot in four unseen environments with an average success rate of 75%. Therefore, the proposed mapless LiDAR navigation control system is effective for robot navigation control in an unknown environment without the global map.

To improve the efficiency and accuracy of the indoor multi-robot cooperative mapping, the extraction method of overlapping regions between each local map based on the geometric features is proposed. First, the geometric features of local maps are extracted and fitted to represent the semantic information of the indoor building structure. Second, the attribute similarity evaluation, the topology similarity evaluation, and the ICP evaluation are used to extract the most similar frame pair between each local map. Third, the local map is merged by overlapping area registration to construct a global map. The proposed method is verified in three different real-world scenarios, the experimental result shows that the efficiency and accuracy of the cooperative mapping method are improved compared with the single-robot mapping method and the existing cooperative mapping method.

The 1st Chinese carbon dioxide (CO2) monitoring satellite mission, TanSat, was launched in 2016. The 1st TanSat global map of CO2 dry-air mixing ratio (XCO2) measurements over land was released as version 1 data product with an accuracy of 2.11 ppmv (parts per million by volume). In this paper, we introduce a new (version 2) TanSat global XCO2 product that is approached by the Institute of Atmospheric Physics Carbon dioxide retrieval Algorithm for Satellite remote sensing (IAPCAS), and the European Space Agency (ESA) Climate Change Initiative plus (CCI+) TanSat XCO2 product by University of Leicester Full Physics (UoL-FP) retrieval algorithm. The correction of the measurement spectrum improves the accuracy (@#@0.08 ppmv) and precision (1.41 ppmv) of the new retrieval, which provides opportunity for further application in global carbon flux studies in the future. Inter-comparison between the two retrievals indicates a good agreement, with a standard deviation of 1.28 ppmv and a bias of −0.35 ppmv.

Owing to their short communication range, wireless nodes in wireless sensor networks (WSNs) can exchange information with devices in their vicinity only. Thus, in sparse networks, the full connectivity of the network is rarely achieved. This renders a centralised approach towards localisation in WSNs useless. Moreover, the exploitation of a centralised algorithm for localisation compromises the scalability in dense networks. Thus, a decentralised, location-aware network with partial connectivity and hybrid (range and direction) measurements obtained between known sensors (reference sensors) and sensors at unknown locations (target sensors) is under focus. The decentralised location estimation is obtained using a linear least squares (LLS) approach and performance enhancements are achieved by introducing a weighing strategy to produce weighted least squares (WLS) estimates. This distributed estimation is made possible by designing a map stitching technique that forms the global map of the network from individual local maps of the wireless nodes without compromising the distributed nature of the network. In the analytical section of the study, theoretical mean squares error expression is derived for LLS estimation, and a Cramer–Rao lower bound is derived to bind the performance of the WLS solution. The algorithm's performance validation is conducted both theoretically and via simulations.