Automatic Mapping Research Articles

Combining rover images and satellite data in the process of automatic mapping of aeolian formations on the Martian Surface

Combining rover images and satellite data in the process of automatic mapping of aeolian formations on the Martian Surface

Using spatial video and deep learning for automated mapping of ground-level context in relief camps

BackgroundThe creation of relief camps following a disaster, conflict or other form of externality often generates additional health problems. The density of people in a highly stressed environment with questionable safe food and water access presents the potential for infectious disease outbreaks. These camps are also not static data events but rather fluctuate in size, composition, and level and quality of service provision. While contextualized geospatial data collection and mapping are vital for understanding the nature of these camps, various challenges, including a lack of data at the required spatial or temporal granularity, as well as the issue of sustainability, can act as major impediments. Here, we present the first steps toward a deep learning-based solution for dynamic mapping using spatial video (SV).MethodsWe trained a convolutional neural network (CNN) model on a SV dataset collected from Goma, Democratic Republic of Congo (DRC) to identify relief camps from video imagery. We developed a spatial filtering approach to tackle the challenges associated with spatially tagging objects such as the accuracy of global positioning system and positioning of camera. The spatial filtering approach generates smooth surfaces of detection, which can further be used to capture changes in microenvironments by applying techniques such as raster math.ResultsThe initial results suggest that our model can detect temporary physical dwellings from SV imagery with a high level of precision, recall, and object localization. The spatial filtering approach helps to identify areas with higher concentrations of camps and the web-based tool helps to explore these areas. The longitudinal analysis based on applying raster math on the detection surfaces revealed locations, which had a considerable change in the distribution of tents over space and time.ConclusionsThe results lay the groundwork for automated mapping of spatial features from imagery data. We anticipate that this work is the building block for a future combination of SV, object identification and automatic mapping that could provide sustainable data generation possibilities for challenging environments such as relief camps or other informal settlements.

Automatic Orbital Pair Selection for Multilevel Local Coupled-Cluster Based on Orbital Maps.

We present an automatic, orbital-map based orbital-pair selection scheme for multilevel local coupled-cluster approaches that exploits the locality of chemical reactions by focusing on the part of the molecule directly involved in the reaction. The previously introduced pair-selected multilevel extension to domain-based local pair natural orbital coupled-cluster with singles, doubles, and semicanonical perturbative triples [DLPNO-CCSD(T0)] partitions the orbital pairs according to relative changes in pair correlation energies [Bensberg, M.; Neugebauer, J. Orbital pair selection for relative energies in the domain-based local pair natural orbital coupled-cluster method. J. Chem. Phys. 2022, 157, 064102. 10.1063/5.0100010]. To this end, maps between localized orbitals are required which in turn require maps between the atoms of structures along reaction paths. So far, these atom maps have been manually determined, which can be a (human) time-consuming procedure. Here, we present an automatic atom mapping algorithm based on the principle of minimum chemical distance that incorporates orientation dependence through dihedral angles. A similar strategy is then introduced to obtain orbital maps, which proves advantageous over the previously used direct orbital selection. Along with a modified orbital pair prescreening, this results in an improved variant of the pair-selected multilevel DLPNO-CCSD(T0) method. The performance of this approach is demonstrated for various reaction types showing a significant efficiency gain and accurate results due to beneficial, systematic error cancellation. The presented method operates in a black-box manner due to its fully automatized algorithms with only the need to specify a single target-accuracy parameter. Additionally, we demonstrate that basis set extrapolation techniques can be applied. In this context, the approach shows deficiencies for the use of large basis sets, especially with diffuse basis functions, which can be traced back to the semicanonical triples correction.

A comparison of optical remote sensing data for automatic fracture network mapping in Lokoja region, Central Nigeria

A comparison of optical remote sensing data for automatic fracture network mapping in Lokoja region, Central Nigeria

Atrial pace mapping with automatic intracardiac pattern matching for non-pulmonary vein trigger ablation: accuracy and feasibility

Abstract Atrial pace mapping (APM) is an alternative method to localize non-pulmonary vein (non-PV) triggers of atrial fibrillation(AF) that are otherwise not eligible for activation mapping due to their instability. The technique applies pattern matching of p-waves and intracardiac signals of atrial trigger and pacing from the site of the suspected origin. A new automated algorithm for intracardiac (IC) pattern matching was recently introduced into the 3D mapping system using the unipolar signal of the reference catheter. The study aims to assess the accuracy and feasibility of APM using IC-pattern matching for localizing non-PV triggers. Methods We assessed the precision of APM using a single chamber and double chamber reference in 20 patients who underwent index pulmonary vein isolation. APM was performed at the anterior and posterior wall of the left atrium (LApost, Laant) and the posterior and lateral wall of the right atrium (Rapost, Ralat). The first pacing point was declared as the origin. The IC-pattern matching algorithm was applied to calculate the similarity of the IC-pattern of the origin to the paced IC-pattern across various distances. The distribution of IC-pattern similarities was analyzed in the Python environment. The feasibility of APM was tested in patients who underwent REDO ablation of AF if all PVs were isolated and no low-voltage substrate was detected. The induction protocol for non-PV triggers included programmed and burst atrial pacing, high-dose isoproterenol, and adenosine infusion. If the IC pattern of the trigger was reproducible, the IC pattern matching was conducted. Ablation was performed at the site of the best IC-pattern matching. Results Atrial PM with IC-pattern matching and a single referent catheter showed lower precision in the RA lateral region (the median[Q1; Q3 ]area of 5% best matching score 1.48 [0.76; 2.01], 0.62[0.15; 1.06], 0.6[0.38; 1.19] and 5.68[2.89; 11.1] cm2 for LApost, LAant, RApost, RAlat accordingly, p = 0.0316). IC-pattern matching with double referent catheter showed high precision at all four anatomical regions (the median [Q1; Q3] area of 5% best matching score 0.126 [0.0879; 0.356], 0.486 [0.205; 0.717], 0.269 [0.198; 0.719], and 0.257 [0.0740; 0.572] cm2 for LApost, LAant, RApost, RAlat accordingly, Fig. 1). Seven out of 37 patients referred to REDO ablation had all PV isolated and absence of low-voltage substrate. In 3 of them, sustained tachycardias (AVNRT, focal atrial tachycardia and typical atrial flutter) were induced, mapped, and ablated. Non-sustained FAT from the RA posterior wall and PAC in the RA septum were induced under the high-dose isoproterenol infusion in two patients. Atrial pace mapping with double chamber referent and successful ablation of these non-PV triggers were performed (Fig.2). Conclusion The atrial pace mapping of non-PV triggers employing the automatic intracardiac pace mapping algorithm is feasible with high accuracy if a double-chamber referent is used.Accuracy of atrial pace mappingNon-PV trigger pace mapping

Remote sensing and machine learning algorithms to predict soil salinity in southern Kazakhstan

Salinization and land degradation are significant challenges in the southern regions of Kazakhstan. These issues arise due to climate change, unequal water resource distribution, and human impact. The primary concern revolves around water resources, which are influenced by the area’s trans boundary flow of major rivers. The low level of water and food security has pushed the development of new approaches based on remote sensing monitoring and geographic information systems (GIS) to provide solutions for soil salinity. The research aims to focus on utilizing high-resolution radar images. This data type is effective for cloudy weather and can be useful for continued monitoring of some areas. Machine learning methods can solve the problem of automatic mapping of agricultural land salinity in Kazakhstan’s southern regions. The precise mapping of the salinity area helps prevent or decrease salinity’s impact on agriculture. The experiment realized that complex models such as LightGBM do not have significant accuracy performance over simple models on a small dataset compared with Ridge regression. The results allow us to recommend an approach for further improvement with ground-based measurement data and other deep-learning methods for mapping the salinity of agricultural lands.

Accuracy of endocardial abnormal electrogram characterization using a 17-AHA-segmental late boundary and voltage-dependent sensitivity annotation for ventricular tachycardia ablation

Abstract Background Substrate-based catheter ablation of ventricular tachycardia (VT) has become the mainstay therapy for patients with scar-related VT. Correct identification and timing of abnormal near-field EGMs and discerning it from far-field EGMs, is key for procedural success. The novel CARTO™ Late Activation Mapping (LAM) module provides fast and accurate annotation of near-field EGMs including late abnormal ventricular activation. For the algorithm to succeed, a late boundary (LB) annotation line should be positioned immediately behind the normal near-field EGM, however, since the timing of normal local near-field activation varies greatly throughout the endocardium, there is currently no consensus for the optimal LB positioning. We hypothesized that 17-AHA segmental annotation of the endocardial LB followed by scar-dependent sensitivity settings, will improve the accuracy of the near-field EGM characterization. Purpose To evaluate the accuracy of endocardial abnormal EGM characterization using a 17-AHA-segmental late boundary and voltage-dependent sensitivity annotation as compared to conventional mid-QRS LB positioning. Methods Five high-resolution substrate voltage maps were obtained from 5 patients (80% male, 68±7 years old, 80% ischemic cardiomyopathy) that underwent a catheter-based ablation for scar-related VT. A personalized CT-derived anatomical reconstruction of the 17-segment AHA model was co-registered offline with the electroanatomic voltage map, using ADAS3D software. Per segment, individual LB timings were set at the highest sensitivity. A gold standard annotation map was created by two independent experts after manual scrutiny of individual EGMs. Next, the expert map was compared to automatic maps created from segmental LB and mid-QRS LB positions at normal, high, highest or mixed (normal/high or normal/highest) sensitivities, focusing on EGM voltage and activation accuracy. Results A mean of 2041±1028 points per map were analyzed. Correct correlation of EGM annotation was found on average in 91% (activation) and 81% (voltage) for all segmental LB positioned maps (segLB), and in 83% (activation) and 77% (voltage) for all mid-QRS LB positioned maps (mid-QRSLB). The highest correlation with expert map for both activation and voltage annotation was found in the mixed sensitivity normal/highest map with segmental LB positioning (activation 98%, voltage 88%). The correlation on activation was significantly superior in the segLB map when compared to the normal sensitivity maps (p=0.0008 for mid-QRSLB and p=0.02 for segLB). For correlation on voltage, the segLBmap with mixed sensitivity was significantly superior compared to three mid-QRSLB maps at normal (p=0.04), high and highest (p=0.01) sensitivity. Conclusion Individualized, 17-AHA-segmental late boundary and voltage-dependent sensitivity annotation provides a possible accurate and clinically-relevant framework for substrate assessment. Prospective validation is needed.

An Automatic Mapping Method of Navigation Map for Outdoor Road Scenes

Abstract. In the process of autonomous navigation of outdoor mobile robots, four modules are involved: perception, localization, planning, and controlling. The perception module utilizes sensors such as cameras and radars based on various principles to analyze the robot's surroundings in real-time. The localization module uses GPS (Global Positioning System), IMU (Inertial Measurement Unit), and prior maps for real-time positioning analysis. The planning module plans optimal path based on the outputs of the first two modules, and the controlling module directs the robot's chassis to move along the planned optimal path. For the localization module, the accuracy of GPS positioning results heavily depends on weather conditions and GPS signal receptions. Even if the positioning results of imu are integrated, the positioning accuracy still cannot meet the needs of robot navigation. Therefore, using prior maps for repositioning can compensate for this accuracy deficiency. The planning module also requires path planning based on prior maps. If the a priori map storage is large, it will lead to difficulties in usage, maintenance, and updates. Therefore, it is crucial to research lightweight navigation map mapping methods. In this paper, an automatically mapping method of lightweight navigation maps is proposed, combining cameras and LiDAR (Light Detection and Ranging), including semantic informations necessary for outdoor navigation positioning, such as pole-like objects and traffic signs for robot longitudinal positioning, and lane line elements for robot lateral positioning. This method automatic generates robot navigation maps in Lanelet2 format, providing support for subsequent positioning and path planning modules.

Unsupervised Change Detection Methods Applied to Landslide Mapping: Case Study in São Sebastião, Brazil

ABSTRACTLandslides represent a growing global geological hazard, further intensified by climate‐induced changes. Remote sensing data, through its capacity for repetitive collection and change detection techniques, that compare and quantify the spatio‐temporal alterations over time, plays a critical role in landslide detection. Considering the February 2023 São Sebastião event and Sentinel‐2 imagery, we assessed diverse unsupervised change detection techniques, encompassing both traditional and recent machine learning‐based approaches. Notably, the Floating References (FR) and Homogeneous Blocks Single‐class Classification (HBSC) methods outperform classic approaches and deliver the most accurate results with F1‐Score and kappa coefficient exceeding 0.96 and 0.92, respectively. These outcomes demonstrate the efficacy of machine learning in automating landslide delineation and underscore the necessity of meticulous data and parameter selection in achieving high‐accuracy automatic landslide mapping. Lastly, this study fills a significant gap in the existing literature by evaluating unsupervised change detection methods for landslide mapping within the Brazilian context.

A phenological-knowledge-independent method for automatic paddy rice mapping with time series of polarimetric SAR images

Paddy rice, which sustains more than half of the global population, requires accurate and efficient mapping to ensure food security. Synthetic aperture radar (SAR) has become indispensable in this process due to its remarkable ability to operate effectively in adverse weather conditions and its sensitivity to paddy rice growth. Phenological-knowledge-based (PKB) methods have been commonly employed in conjunction with time series of SAR images for paddy rice mapping, primarily because they eliminate the need for training datasets. However, PKB methods possess inherent limitations, primarily stemming from their reliance on precise phenological information regarding paddy rice growth. This information varies across regions and paddy rice varieties, making it challenging to use PKB methods effectively on a large spatial scale, such as the national or global scale, where collecting comprehensive phenological data becomes impractical. Moreover, variations in farming practices and field conditions can lead to differences in paddy rice growth stages even within the same region. Using a generalized set of phenological knowledge in PKB methods may not be suitable for all paddy fields, potentially resulting in errors in paddy rice extraction. To address the challenges posed by PKB methods, this study proposed an innovative approach known as the phenological-knowledge-independent (PKI) method for mapping paddy rice using time series of Sentinel-1 SAR images. The central innovation of the PKI method lies in its capability to map paddy rice without relying on specific knowledge of paddy rice phenology or the need for a training dataset. This was made possible by the incorporation of three novel metrics: VH and VV normalized maximum temporal changes (NMTC) and VH temporal mean, derived from the distinctions between paddy rice and other land cover types in time series of SAR images. The PKI method was rigorously evaluated across three regions in China, each featuring different paddy rice varieties. Additionally, the PKI method was compared with two prevalent phenological-knowledge-based techniques: the automated paddy rice mapping method using SAR flooding signals (ARM-SARFS) and the manual interpretation of unsupervised clustering results (MI-UCR). The PKI method achieved an average overall accuracy of 97.99%, surpassing the ARM-SARFS, which recorded an accuracy of 89.65% due to errors stemming from phenological disparities among different paddy fields. Furthermore, the PKI method delivered results on par with the MI-UCR, which relied on the fusion of SAR and optical image time series, achieving an accuracy of 97.71%. As demonstrated by these findings, the PKI method proves highly effective in mapping paddy rice across diverse regions, all without the need for phenological knowledge or a training dataset. Consequently, it holds substantial promise for efficiently mapping paddy rice on a large spatial scale. The source code used in this study is available at https://code.earthengine.google.com/f82cf10cad64fa3f971ae99027001a6e.

Nanoscale Os isotopic quantification of Wadi Tayin dunite platinum group minerals by atom probe tomography

Serpentinites record processes that redistribute major and trace elements between mantle and crust. Platinum Group Elements (PGEs) are trace elements in serpentinites hosted in sulphides and alloys. Alloys are challenging to find, and most analytical techniques lack the spatial resolution to analyse them; hence, this research adopts automatic mineral mapping technique to detect PGM grains in a sample from the Wadi Tayin (Oman) peridotite and uses atom probe tomography, a nanoscale quantitative analytical technique, to analyse the grain. This work applies and assesses the applicability of atom probe tomography to measure the 187Os/188Os isotopic ratio of natural alloys and uses the ratio to constrain the source of Os. A novel algorithm is used to automatically determine the number of counts of the 187Os and 188Os peaks, to calculate the isotopic 187Os/188Os ratio and the analytical uncertainty. The 187Os/188Os ratio is 0.126 ± 0.003, consistent with the isotopic composition reported by literature in the dunite of the Main Mantle Section of the Wadi Tayin ophiolite.The analytical uncertainty is one order of magnitude higher than conventional bulk rock techniques, such as negative-thermal ionisation mass spectrometry (N-TIMS) and inductively coupled plasma mass spectrometry (ICP-MS). However, the precision is sufficient to conclude that the non-radiogenic 187Os/188Os ratio is compatible with a mantle origin for the alloy. Decreasing whole-rock Re with increasing LOI and the overprinting of magmatic pentlandite by magnetite demonstrate that progressive serpentinisation may have modified the Re budget. The results indicate that atom probe tomography can analyse 187Os/188Os ratio quantitatively in micron-sized natural alloys and provide insights into natural processes.

DockQ v2: improved automatic quality measure for protein multimers, nucleic acids, and small molecules.

It is important to assess the quality of modeled biomolecules to benchmark and assess the performance of different prediction methods. DockQ has emerged as the standard tool for assessing the quality of protein interfaces in model structures against given references. However, as predictions of large multimers with multiple chains become more common, DockQ needs to be updated with more functionality for robustness and speed. Moreover, as the field progresses and more methods are released to predict interactions between proteins and other types of molecules, such as nucleic acids and small molecules, it becomes necessary to have a tool that can assess all types of interactions. Here, we present a complete reimplementation of DockQ in pure Python. The updated version of DockQ is more portable, faster and introduces novel functionalities, such as automatic DockQ calculations for multiple interfaces and automatic chain mapping with multi-threading. These enhancements are designed to facilitate comparative analyses of protein complexes, particularly large multi-chain complexes. Furthermore, DockQ is now also able to score interfaces between proteins, nucleic acids, and small molecules. DockQ v2 is available online at: https://wallnerlab.org/DockQ.

Tactile Simultaneous Localization and Mapping Using Low-Cost, Wearable LiDAR

Tactile maps are widely recognized as useful tools for mobility training and the rehabilitation of visually impaired individuals. However, current tactile maps lack real-time versatility and are limited because of high manufacturing and design costs. In this study, we introduce a device (i.e., ClaySight) that enhances the creation of automatic tactile map generation, as well as a model for wearable devices that use low-cost laser imaging, detection, and ranging (LiDAR,) used to improve the immediate spatial knowledge of visually impaired individuals. Our system uses LiDAR sensors to (1) produce affordable, low-latency tactile maps, (2) function as a day-to-day wayfinding aid, and (3) provide interactivity using a wearable device. The system comprises a dynamic mapping and scanning algorithm and an interactive handheld 3D-printed device that houses the hardware. Our algorithm accommodates user specifications to dynamically interact with objects in the surrounding area and create map models that can be represented with haptic feedback or alternative tactile systems. Using economical components and open-source software, the ClaySight system has significant potential to enhance independence and quality of life for the visually impaired.

Using of high spatial resolution images to evaluate the thematic accuracy of land use and occupation maps with the Kappa index

The objective from this article evaluates the thematic quality of automatic mappings with supervised classification for land use and land cover, using high spatial resolution satellite images as "ground truth". This consider the advancement of remote sensing technologies has enabled the acquisition of satellite images with various spatial resolutions, which are essential for thematic mapping and automatic classifiers in the context of land use and land cover mapping. In fact, the wide availability of high, medium, and low spatial resolution satellite images has significantly optimized the time and resources required by using more accurate classifiers during data processing. The image used for verification of this paper was GeoEye, with a spatial resolution of 0.5m, dated October 2023. The images submitted to the automatic classifier were Sentinel-2A with a spatial resolution of 10m and Planet with a spatial resolution of 5m, both from the same satellite pass period (October 2023) over the study area, aiming to avoid seasonal and phenological variations in vegetation, as well as changes in the environment due to anthropogenic intervention. The classification method adopted was Maximum Likelihood (MAXVER). The classification accuracy was rigorously evaluated to ensure the reliability of the results using the Kappa index, assessing the agreement between the observed and expected classifications. Based on the methods presented, the set of mapped classes in this study showed good accuracy for the Planet image and very good accuracy for the Sentinel image.

Towards an automatic UAS-based mapping tool for first responders: Defibrillator Missions in Alpine Regions

Towards an automatic UAS-based mapping tool for first responders: Defibrillator Missions in Alpine Regions

A Novel Approach for Farmland Size Estimation in Small-Scale Agriculture Using Edge Counting and Remote Sensing

Accurate and timely information on farmland size is crucial for agricultural development, resource management, and other related fields. However, there is currently no mature method for estimating farmland size in smallholder farming areas. This is due to the small size of farmland plots in these areas, which have unclear boundaries in medium and high-resolution satellite imagery, and irregular shapes that make it difficult to extract complete boundaries using morphological rules. Automatic farmland mapping algorithms using remote sensing data also perform poorly in small-scale farming areas. To address this issue, this study proposes a farmland size evaluation index based on edge frequency (ECR). The algorithm utilizes the high temporal resolution of Sentinel-2 satellite imagery to compensate for its spatial resolution limitations. First, all Sentinel-2 images from one year are used to calculate edge frequencies, which can divide farmland areas into low-value farmland interior regions, medium-value non-permanent edges, and high-value permanent edges (PE). Next, the Otsu’s thresholding algorithm is iteratively applied twice to the edge frequencies to first extract edges and then permanent edges. The ratio of PE to cropland (ECR) is then calculated. Using the North China Plain and Northeast China Plain as study areas, and comparing with existing farmland size datasets, the appropriate estimation radius for ECR was determined to be 1600 m. The study found that the peak ECR value for the Northeast China Plain was 0.085, and the peak value for the North China Plain was 0.105. The overall distribution was consistent with the reference dataset.

Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models

BackgroundVaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and generate memory cells to defend against targeted diseases. Clinical trials evaluate vaccine performance, including dosage, administration routes, and potential side effects. ClinicalTrials.gov is a valuable repository of clinical trial information, but the vaccine data in them lacks standardization, leading to challenges in automatic concept mapping, vaccine-related knowledge development, evidence-based decision-making, and vaccine surveillance.ResultsIn this study, we developed a cascaded framework that capitalized on multiple domain knowledge sources, including clinical trials, the Unified Medical Language System (UMLS), and the Vaccine Ontology (VO), to enhance the performance of domain-specific language models for automated mapping of VO from clinical trials. The Vaccine Ontology (VO) is a community-based ontology that was developed to promote vaccine data standardization, integration, and computer-assisted reasoning. Our methodology involved extracting and annotating data from various sources. We then performed pre-training on the PubMedBERT model, leading to the development of CTPubMedBERT. Subsequently, we enhanced CTPubMedBERT by incorporating SAPBERT, which was pretrained using the UMLS, resulting in CTPubMedBERT + SAPBERT. Further refinement was accomplished through fine-tuning using the Vaccine Ontology corpus and vaccine data from clinical trials, yielding the CTPubMedBERT + SAPBERT + VO model. Finally, we utilized a collection of pre-trained models, along with the weighted rule-based ensemble approach, to normalize the vaccine corpus and improve the accuracy of the process. The ranking process in concept normalization involves prioritizing and ordering potential concepts to identify the most suitable match for a given context. We conducted a ranking of the Top 10 concepts, and our experimental results demonstrate that our proposed cascaded framework consistently outperformed existing effective baselines on vaccine mapping, achieving 71.8% on top 1 candidate’s accuracy and 90.0% on top 10 candidate’s accuracy.ConclusionThis study provides a detailed insight into a cascaded framework of fine-tuned domain-specific language models improving mapping of VO from clinical trials. By effectively leveraging domain-specific information and applying weighted rule-based ensembles of different pre-trained BERT models, our framework can significantly enhance the mapping of VO from clinical trials.

The AutoICE Challenge

Abstract. Mapping sea ice in the Arctic is essential for maritime navigation, and growing vessel traffic highlights the necessity of the timeliness and accuracy of sea ice charts. In addition, with the increased availability of satellite imagery, automation is becoming more important. The AutoICE Challenge investigates the possibility of creating deep learning models capable of mapping multiple sea ice parameters automatically from spaceborne synthetic aperture radar (SAR) imagery and assesses the current state of the automatic-sea-ice-mapping scientific field. This was achieved by providing the tools and encouraging participants to adopt the paradigm of retrieving multiple sea ice parameters rather than the current focus on single sea ice parameters, such as concentration. The paper documents the efforts and analyses, compares, and discusses the performance of the top-five participants’ submissions. Participants were tasked with the development of machine learning algorithms mapping the total sea ice concentration, stage of development, and floe size using a state-of-the-art sea ice dataset with dual-polarised Sentinel-1 SAR images and 22 other relevant variables while using professionally labelled sea ice charts from multiple national ice services as reference data. The challenge had 129 teams representing a total of 179 participants, with 34 teams delivering 494 submissions, resulting in a participation rate of 26.4 %, and it was won by a team from the University of Waterloo. Participants were successful in training models capable of retrieving multiple sea ice parameters with convolutional neural networks and vision transformer models. The top participants scored best on the total sea ice concentration and stage of development, while the floe size was more difficult. Furthermore, participants offered intriguing approaches and ideas that could help propel future research within automatic sea ice mapping, such as applying high downsampling of SAR data to improve model efficiency and produce better results.

Automatic mapping of winter wheat planting structure and phenological phases using time-series sentinel data

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.

Is Your Training Data Really Ground Truth? A Quality Assessment of Manual Annotation for Individual Tree Crown Delineation

For the accurate and automatic mapping of forest stands based on very-high-resolution satellite imagery and digital orthophotos, precise object detection at the individual tree level is necessary. Currently, supervised deep learning models are primarily applied for this task. To train a reliable model, it is crucial to have an accurate tree crown annotation dataset. The current method of generating these training datasets still relies on manual annotation and labeling. Because of the intricate contours of tree crowns, vegetation density in natural forests and the insufficient ground sampling distance of the imagery, manually generated annotations are error-prone. It is unlikely that the manually delineated tree crowns represent the true conditions on the ground. If these error-prone annotations are used as training data for deep learning models, this may lead to inaccurate mapping results for the models. This study critically validates manual tree crown annotations on two study sites: a forest-like plantation on a cemetery and a natural city forest. The validation is based on tree reference data in the form of an official tree register and tree segments extracted from UAV laser scanning (ULS) data for the quality assessment of a training dataset. The validation results reveal that the manual annotations detect only 37% of the tree crowns in the forest-like plantation area and 10% of the tree crowns in the natural forest correctly. Furthermore, it is frequent for multiple trees to be interpreted in the annotation as a single tree at both study sites.