Mapping Framework Research Articles

Mottainai: A holistic value stream approach to create sustainability

This paper takes a holistic look at sustainability through the lens of the Japanese concept of ‘mottainai’, which embraces respect for people and resources that our planet offers. The paper goes on to explain the life cycle assessment (LCA) framework for mapping and analysis of end-to-end value stream activities. This tool helps to identify, measure and develop a comprehensive action plan at the point of occurrence effectively targeting greenhouse gas (GHG) emissions. This can be done only in collaboration with network partners with clear scorecard-based evaluation and monitoring. Using examples from the automotive industry and a case study from a healthcare pharmaceutical drug producer, it weaves the concepts of LCA, value stream mapping and scorecard seamlessly into something which the reader may find it easy and simple to implement. The paper also touches upon the challenges one needs to be cognisant of in implementing changes in the network to achieve sustainable supply chain operations, with a firm belief that the journey towards nature-positive is not only the need of the hour but is also possible practically. We owe it to not just future generations but also to ourselves within our lifetime.

A Hybrid LiDAR-Based Mapping Framework for Efficient Path Planning of AGVs in a Massive Indoor Environment

A Hybrid LiDAR-Based Mapping Framework for Efficient Path Planning of AGVs in a Massive Indoor Environment

A stacked ensemble learning-based framework for mineral mapping using AVIRIS-NG hyperspectral image

A stacked ensemble learning-based framework for mineral mapping using AVIRIS-NG hyperspectral image

A Dynamic Visual SLAM System Incorporating Object Tracking for UAVs

The capability of unmanned aerial vehicles (UAVs) to capture and utilize dynamic object information assumes critical significance for decision making and scene understanding. This paper presents a method for UAV relative positioning and target tracking based on a visual simultaneousocalization and mapping (SLAM) framework. By integrating an object detection neural network into the SLAM framework, this method can detect moving objects and effectively reconstruct the 3D map of the environment from image sequences. For multiple object tracking tasks, we combine the region matching of semantic detection boxes and the point matching of the optical flow method to perform dynamic object association. This joint association strategy can prevent trackingoss due to the small proportion of the object in the whole image sequence. To address the problem ofacking scale information in the visual SLAM system, we recover the altitude data based on a RANSAC-based plane estimation approach. The proposed method is tested on both the self-created UAV dataset and the KITTI dataset to evaluate its performance. The results demonstrate the robustness and effectiveness of the solution in facilitating UAV flights.

Spatiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand

Floods cause disastrous damage to the environment, economy, and humanity. Flood losses can be reduced if adequate management is implemented in the pre-disaster period. Flood hazard maps comprise disaster risk information displayed on geo-location maps and the potential flood events that occur in an area. This paper proposes a spatiotemporal flood hazard map framework to generate a flood hazard map using spatiotemporal data. The framework has three processes: (1) temporal prediction, which uses the LSTM technique to predict water levels and rainfall for the next time; (2) spatial interpolation, which uses the IDW technique to estimate values; and (3) map generation, which uses the CNN technique to predict flood events and generate flood hazard maps. The study area is Chiang Mai Province, Thailand. The generated hazard map covers 20,107 km2. There are 14 water-level telemetry stations and 16 rain gauge stations. The proposed model accurately predicts water level and rainfall, as demonstrated by the evaluation results (RMSE, MAE, and R2). The generated map has a 95.25% mean accuracy and a 97.25% mean F1-score when compared to the actual flood event. The framework enhances the accuracy and responsiveness of flood hazard maps to reduce potential losses before floods occur.

Enhanced Unmanned Aerial Vehicle Localization in Dynamic Environments Using Monocular Simultaneous Localization and Mapping and Object Tracking

This work proposes an innovative approach to enhance the localization of unmanned aerial vehicles (UAVs) in dynamic environments. The methodology integrates a sophisticated object-tracking algorithm to augment the established simultaneous localization and mapping (ORB-SLAM) framework, utilizing only a monocular camera setup. Moving objects are detected by harnessing the power of YOLOv4, and a specialized Kalman filter is employed for tracking. The algorithm is integrated into the ORB-SLAM framework to improve UAV pose estimation by correcting the impact of moving elements and effectively removing features connected to dynamic elements from the ORB-SLAM process. Finally, the results obtained are recorded using the TUM RGB-D dataset. The results demonstrate that the proposed algorithm can effectively enhance the accuracy of pose estimation and exhibits high accuracy and robustness in real dynamic scenes.

A sound map of Port Hope

ABSTRACT The Hamilton Perambulatory Unit (HPU) is an artist-research collective that has been holding public walking and mapping events since 2014. Our participatory mapping framework that we call the ‘strata-walk’ focuses attention on the multiplicity of sensory, material, imaginative, socio-political and historical layers that make up place, usually resulting in the creation of maps that document a particular place and time through the contribution of our workshop members. In this short essay, I focus specifically on Strata-Walking Port Hope, the HPU’s community sound-mapping project in Port Hope, Ontario, which was commissioned by Critical Mass: A Centre for Contemporary Art, and produced through conversations with over two dozen Port Hopians of all ages. The outcomes include a mobile locative media sound application that maps the resulting sound collages composed of snippets of conversation, site field recordings and found sounds. Strata-Walking Port Hope provides a glimpse into the memories, stories, histories, and sonic environments of Port Hope.

Improved Wetland Mapping of a Highly Fragmented Agricultural Landscape Using Land Surface Phenological Features

Wetlands are integral components of agricultural landscapes, providing a wide range of ecological, economic, and social benefits essential for sustainable development and rural livelihoods. Globally, they are vulnerable ecological assets facing several significant threats including water extraction and regulation, land clearing and reclamation, and climate change. Classification and mapping of wetlands in agricultural landscapes is crucial for conserving these ecosystems to maintain their ecological integrity amidst ongoing land-use changes and environmental pressures. This study aims to establish a robust framework for wetland classification and mapping in intensive agricultural landscapes using time series of Sentinel-2 imagery, with a focus on the Gwydir Wetland Complex situated in the northern Murray–Darling Basin—Australia’s largest river system. Using the Google Earth Engine (GEE) platform, we extracted two groups of predictors based on six vegetation indices time series calculated from multi-temporal Sentinel-2 surface reflectance (SR) imagery: the first is statistical features summarizing the time series and the second is phenological features based on harmonic analysis of time series data (HANTS). We developed and evaluated random forest (RF) models for each level of classification with combination of different groups of predictors. Our results show that RF models involving both HANTS and statistical features perform strongly with significantly high overall accuracy and class-weighted F1 scores (p < 0.05) when comparing with models with either statistical or HANTS variables. While the models have excellent performance (F-score greater than 0.9) in distinguishing wetlands from other landcovers (croplands, terrestrial uplands, and open waters), the inter-class discriminating power among wetlands is class-specific: wetlands that are frequently inundated (including river red gum forests and wetlands dominated by common reed, water couch, and marsh club-rush) are generally better identified than the ones that are flooded less frequently, such as sedgelands and woodlands dominated by black box and coolabah. This study demonstrates that HANTS features extracted from time series Sentinel data can significantly improve the accuracy of wetland mapping in highly fragmentated agricultural landscapes. Thus, this framework enables wetland classification and mapping to be updated on a regular basis to better understand the dynamic nature of these complex ecosystems and improve long-term wetland monitoring.

Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets

ABSTRACT 2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping and exoribonuclease (DXO) protein, and the biogenesis and specificity of rRNA. Recent advancements in single-molecule sequencing techniques for long read RNA sequencing data offered by Oxford Nanopore technologies have enabled the direct detection of RNA modifications from sequencing data. In this study, we propose a bio-computational framework, Nm-Nano, for predicting the presence of Nm sites in direct RNA sequencing data generated from two human cell lines. The Nm-Nano framework integrates two supervised machine learning (ML) models for predicting Nm sites: Extreme Gradient Boosting (XGBoost) and Random Forest (RF) with K-mer embedding. Evaluation on benchmark datasets from direct RNA sequecing of HeLa and HEK293 cell lines, demonstrates high accuracy (99% with XGBoost and 92% with RF) in identifying Nm sites. Deploying Nm-Nano on HeLa and HEK293 cell lines reveals genes that are frequently modified with Nm. In HeLa cell lines, 125 genes are identified as frequently Nm-modified, showing enrichment in 30 ontologies related to immune response and cellular processes. In HEK293 cell lines, 61 genes are identified as frequently Nm-modified, with enrichment in processes like glycolysis and protein localization. These findings underscore the diverse regulatory roles of Nm modifications in metabolic pathways, protein degradation, and cellular processes. The source code of Nm-Nano can be freely accessed at https://github.com/Janga-Lab/Nm-Nano.

Deep Reinforcement Learning-Based 3D Trajectory Planning for Cellular Connected UAV

To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the 3D space environment and integrating factors such as UAV mission completion time and connectivity, we develop an objective function for path optimization and utilize the advanced dueling double deep Q network (D3QN) to optimize it. Additionally, we introduce the prioritized experience replay (PER) mechanism to enhance learning efficiency and expedite convergence. In order to further aid in trajectory planning, our method incorporates a simultaneous navigation and radio mapping (SNARM) framework that generates simulated 3D radio maps and simulates flight processes by utilizing measurement signals from the UAV during flight, thereby reducing actual flight costs. The simulation results demonstrate that the proposed approach effectively enable UAVs to avoid weak coverage regions in space, thereby reducing the weighted sum of flight time and expected interruption time.

Predictive mapping of organic carbon stocks in surficial sediments of the Canadian continental margin

Abstract. Quantification and mapping of surficial seabed sediment organic carbon have wide-scale relevance for marine ecology, geology and environmental resource management, with carbon densities and accumulation rates being a major indicator of geological history, ecological function and ecosystem service provisioning, including the potential to contribute to nature-based climate change mitigation. While global analyses can appear to provide a definitive understanding of the spatial distribution of sediment carbon, regional maps may be constructed at finer resolutions and can utilise targeted data syntheses and refined spatial data products and therefore have the potential to improve these estimates. Here, we report a national systematic review of data on organic carbon content in seabed sediments across Canada and combine this with a synthesis and unification of the best available data on sediment composition, seafloor morphology, hydrology, chemistry and geographic settings within a machine learning mapping framework. Predictive quantitative maps of mud content, dry bulk density, organic carbon content and organic carbon density were produced along with cell-specific estimates of their uncertainty at 200 m resolution across 4 489 235 km2 of the Canadian continental margin (92.6 % of the seafloor area above 2500 m) (https://doi.org/10.5683/SP3/ICHVVA, Epstein et al., 2024). Fine-scale variation in carbon stocks was identified across the Canadian continental margin, particularly in the Pacific Ocean and Atlantic Ocean regions. Overall, we estimate the standing stock of organic carbon in the top 30 cm of surficial seabed sediments across the Canadian shelf and slope to be 10.9 Gt (7.0–16.0 Gt). Increased empirical sediment data collection and higher precision in spatial environmental data layers could significantly reduce uncertainty and increase accuracy in these products over time.

A Scene Unmixing Deep Learning Network for Local Climate Zone Mapping and Analysis Using Very High Resolution Remote Sensing Imagery

Abstract. With the acceleration of urbanization, the environment and climate necessary for our survival have gradually deteriorated, leading to the increasing prominence of the Urban Heat Island (UHI) effect. Local Climate Zone (LCZ) classification, as a standard of urban morphology, has become an essential tool for monitoring the UHI effect and conducting temperature studies. Deep Learning (DL) models have the ability to represent high-level semantic features. Therefore, this paper proposes amixed scene unmixing DL framework for LCZ mapping and analysis using Very High Resolution (VHR) remote sensing images. This framework consists of a two-stream deep network, including a pure scene classification network (PS-Net) and a mixed scene unmixing network (MSU-Net). We conducted random sampling tests in Wuhan, China in the experiment A. The results show that this model achieved a satisfactory accuracy with the Overall Accuracies (OAs) is 96.78% and a mixed scene unmixing Mean Absolute Error (MAE) of 0.0495. Furthermore, we applied the proposed model to generate LCZ map for five districts in Wuhan in the experiment B. The test accuracy between two experiments differs very slightly. These results demonstrate the applicability and potential of our model for LCZ mapping and urban climate analysis.

Mapping the Landscape of Continuing Professional Development: A Pilot Gap Map of Systematic Reviews

Gap maps graphically represent evidence from systematic reviews. We developed and tested a pilot gap map of systematic reviews examining the impact of continuing professional development (CPD). We conducted a mapping review and charted the content of systematic reviews in a pilot gap map. Directive content analysis helped develop categories in three dimensions: evaluation framework, study characteristics, and number of systematic reviews. Of 389 identified records, 29 systematic reviews were included in the pilot map. We found gaps in evidence evaluating the impact of e-learning and skills training programs on various outcomes (patient, care, and work environment). Most evaluations corresponded to Kirkpatrick's learning model, with few considering other outcomes or how CPD could be mediated or influenced by contextual factors. Rooted in social epidemiological principles, the pilot map framework introduced new ways of understanding evidence from systematic reviews. Although the pilot map illuminated several evidence gaps and perspectives important to future CPD, a complete map is needed to validate the findings.

A 30 m annual cropland dataset of China from 1986 to 2021

A 30 m annual cropland dataset of China from 1986 to 2021

Unraveling near real-time spatial dynamics of population using geographical ensemble learning

Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.

A new covariance intersection based integrated SLAM framework for 3D outdoor agricultural applications

AbstractThis letter introduces a novel integrated framework for simultaneous localization and mapping (SLAM) tailored for general agricultural applications. The framework combines a cutting‐edge SLAM method, LIO‐SAM, with covariance intersection for sensor fusion. Agricultural robots often operate in unstructured environments with sparse feature points and encounter repeated similar information, such as trees. Therefore, a fusion framework based on 3D SLAM augmented with additional information, such as feature‐independent GPS data, becomes essential. This study proposes an integrated SLAM framework by introducing a convergence strategy based on covariance analysis, incorporating a state‐of‐the‐art 3D SLAM technique. The convergence methods, namely “dynamic weight assignment” and “winner takes all”, are presented alternatively, tailored to seamlessly integrate with the proposed framework. Evaluations using a public dataset and an experiment demonstrate the effectiveness of the approach through numerical analysis and visual representation. The results illustrate that this method surpasses conventional approaches in accurately estimating the robot's position. In the future, this research will focus on automating crop cultivation and harvesting by integrating the proposed system with robot arm control.

农业环境高精度三维点云地图构建方法研究

In agricultural operation scenarios, the diversity of farmland terrain, crops and other forms, as well as uncertain factors such as weather changes and crop growth during agricultural operation, can have an impact on the construction of high-precision maps. To address these challenges and analyze operational scenarios based on the characteristics of agricultural scenarios, this paper proposes a point cloud map construction algorithm for plant point removal and locatability estimation. Based on the existing Simultaneous Localization and Mapping (SLAM) framework, plant point removal and locatability estimation are improved. Firstly, Red, Green, Blue (RGB) images and Near Infrared (NIR) images are fused to identify and remove plant point clouds, preserving effective inter frame matching information, reducing the impact of dynamic points on inter frame matching, and achieving high front-end motion estimation accuracy. Then, the localization estimation method based on learning is used to determine the motion estimation status and determine whether to execute the backend optimization algorithm. Finally, the back-end optimization algorithm based on Factor graph is designed, and the Factor graph, constraint relationship and optimization function are constructed to optimize the pose of all frames. The optimized map construction algorithm reduces the re projection errors between field roads, paths, and crop rows by 10.27%, 20.76%, and 14.36% compared to before optimization. To verify the actual operational effectiveness of the point cloud map construction algorithm, the hardware part of the multi-sensor information collection system was designed, and sensor internal and external parameter calibration were also carried out. A map information collection vehicle was built and field experiments were conducted. The results showed that the positioning error of the point cloud map construction method proposed in this paper is less than 0.5°, and the cumulative error of 30 m translation is less than 12 cm, which meets the actual operational requirements.

An exploratory framework for mapping, mechanism, and management of urban soundscape quality: From quietness to naturalness

BackgroundDespite growing attention from researchers and governments, challenges persist in comprehensively assessing urban sound quality by integrating both quietness and naturalness aspects. GoalsThis study aimed to develop an innovative soundscape quality index that concurrently evaluates quietness and naturalness in urban soundscapes. Our objectives included conducting urban soundscape quality mapping, analyzing influential mechanisms, and identifying priority zones for sound environment management. ApproachesWe collected sound pressure level (SPL) and raw audio data, from which we computed a normalized difference soundscape index (NDSI). With a dataset comprising 28 explanatory variables encompassing land use, built environment, vegetation characteristics, and temporal factors, we employed the random forest (RF) model to predict 10 indicators, including eight SPL-related indices, NDSI, and the QNS (quietness and naturalness soundscape) index. Crucially, we utilized SHAP (SHapley Additive exPlanations) values to interpret the RF model. FindingsSpatial variations in quietness and naturalness were evident, closely associated with road networks and vegetation, respectively, with discernible temporal variations. The top three variables influencing QNS were distance to major roads, normalized difference vegetation index (NDVI), and proportion of tree coverage. Moreover, interaction effects highlighted dual negative or synergistic promoting effects on QNS from factors such as road width, human disturbance, vegetation configurations, and land cover. Notably, these mechanisms were successfully applied to six typical tourist attractions in Xiamen city, where five types of management zones were mapped based on priority considerations of population density and soundscape quality. Interestingly, natural soundscape reserves were highly correlated with city parks, high-risk zones predominantly overlapped with road networks, and potential zones comprised inner communities between streets. SignificanceThe framework demonstrated effectiveness in mapping, exploring mechanisms, and guiding management strategies for the urban sound environment.

A framework for collaborative multi-robot mapping using spectral graph wavelets

The exploration of large-scale unknown environments can benefit from the deployment of multiple robots for collaborative mapping. Each robot explores a section of the environment and communicates onboard pose estimates and maps to a central server to build an optimized global multi-robot map. Naturally, inconsistencies can arise between onboard and server estimates due to onboard odometry drift, failures, or degeneracies. The mapping server can correct and overcome such failure cases using computationally expensive operations such as inter-robot loop closure detection and multi-modal mapping. However, the individual robots do not benefit from the collaborative map if the mapping server provides no feedback. Although server updates from the multi-robot map can greatly alleviate the robotic mission strategically, most existing work lacks them, due to their associated computational and bandwidth-related costs. Motivated by this challenge, this paper proposes a novel collaborative mapping framework that enables global mapping consistency among robots and the mapping server. In particular, we propose graph spectral analysis, at different spatial scales, to detect structural differences between robot and server graphs, and to generate necessary constraints for the individual robot pose graphs. Our approach specifically finds the nodes that correspond to the drift’s origin rather than the nodes where the error becomes too large. We thoroughly analyze and validate our proposed framework using several real-world multi-robot field deployments where we show improvements of the onboard system up to 90% and can recover the onboard estimation from localization failures and even from the degeneracies within its estimation.

Tetris-inspired detector with neural network for radiation mapping

Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.