High-resolution Radar Data Research Articles

Assessment of Deep Learning Based Image Segmentation for Identifying Floating Net Cages from Very High-Resolution Capella Synthetic Aperture Radar (SAR) Data

Assessment of Deep Learning Based Image Segmentation for Identifying Floating Net Cages from Very High-Resolution Capella Synthetic Aperture Radar (SAR) Data

Co-located OLCI optical imagery and SAR altimetry from Sentinel-3 for enhanced Arctic spring sea ice surface classification

The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution Ku-band radar altimetry data over the polar regions up to 81° North. The combination of synthetic aperture radar (SAR) mode altimetry (SRAL instrument) from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer, results in the creation of the first satellite platform that offers coincident optical imagery and SAR radar altimetry. We utilise this synergy between altimetry and imagery to demonstrate a novel application of deep learning to distinguish sea ice from leads in spring. We use SRAL classified leads as training input for pan-Arctic lead detection from OLCI imagery. This surface classification is an important step for estimating sea ice thickness and to predict future sea ice changes in the Arctic and Antarctic regions. We propose the use of Vision Transformers (ViT), an approach adapting the popular deep learning algorithm Transformer, for this task. Their effectiveness, in terms of both quantitative metric including accuracy and qualitative metric including model roll-out, on several entire OLCI images is demonstrated and we show improved skill compared to previous machine learning and empirical approaches. We show the potential for this method to provide lead fraction retrievals at improved accuracy and spatial resolution for sunlit periods before melt onset.

Enhancing infrastructure management with InSAR monitoring

Over the last few years, the management of multi-asset infrastructure over wide regions has experienced a significant evolution due to novel technologies or accessibility to cheaper sensors combined with the Internet of Things (IoT). Interferometric Synthetic Aperture Radar (InSAR) data has proven to be the most cost-effective technology to meet users’ requests. However, InSAR’s large volume of data presents challenges for end-users. Data aggregation and visualisation are needed to streamline operations and information transfer between teams. This proposed methodology seeks to address the challenges associated with InSAR data by integrating InSAR observations with the digital equivalent of existing structures in a repeatable and scalable manner. To achieve this, deformation profiles are generated adaptively, considering the asset’s characteristics, such as width and orientation, as well as the satellite acquisition geometry. In parallel, a two-dimensional approach is followed to associate InSAR information with each structure element and thus organise the information spatially. It should be noted that, even when using high-resolution radar data, it is sometimes challenging to associate radar returns with specific asset segments due to the complex interaction of microwaves with particular targets. Consequently, a key feature of this methodology is to classify InSAR data according to a precise geocoding of each scatterer, including an accurate estimation of its elevation. This methodology makes it possible to analyse many different types of infrastructure, which can be time-consuming for large highway or railway operators. The coverage of the InSAR results on a regional scale, and the semi-automated method gives decision-makers an overview of their structures to send maintenance teams, saving time and costs. This methodology allows for InSAR-derived product analysis, making the results more readable, accessible, and impactful. This type of infrastructure monitoring reflects a new maturity of InSAR in this field, demonstrated in real case studies.

An efficient workflow for modelling high-dimensional spatial extremes

We develop a comprehensive methodological workflow for Bayesian modelling of high-dimensional spatial extremes that lets us describe both weakening extremal dependence at increasing levels and changes in the type of extremal dependence class as a function of the distance between locations. This is achieved with a latent Gaussian version of the spatial conditional extremes model that allows for computationally efficient inference with R-INLA. Inference is made more robust using a post hoc adjustment method that accounts for possible model misspecification. This added robustness makes it possible to extract more information from the available data during inference using a composite likelihood. The developed methodology is applied to the modelling of extreme hourly precipitation from high-resolution radar data in Norway. Inference is performed quickly, and the resulting model fit successfully captures the main trends in the extremal dependence structure of the data. The post hoc adjustment is found to further improve model performance.

Exploring the use of 3D radar measurements in predicting the evolution of single-core convective cells

Object-based radar rainfall nowcasting is a widely used technique for convective storm prediction. Currently, most existing object-based nowcasting methods primarily focus on predicting cell movements, neglecting the temporal evolution of cell properties such as size, shape, and intensity. Incorporating this evolution is critical for improving predictability in convective storms. While previous studies have used three-dimensional (3D) radar observations to capture vertical changes during convective cell formation, these efforts often analyse or reconstruct specific convective events. Integrating 3D radar information into operational object-based radar rainfall nowcasting remains an open challenge. This research addresses this challenge using deep learning (DL) techniques. More specifically, a DL-based prediction model is developed, which uses 2D and 3D cells' properties retrieved from 3D radar reflectivity data at the current time and across the past 15 min to predict the evolution of these properties over the next 15 min. This model could eventually be integrated into existing object-based nowcasting models. A total of 4708 cell lifecycles, extracted from high-resolution (5-min, 1-km, 24 levels at 0.5 km intervals) 3D radar data across the UK, are used to train the model, and a total of 1177 lifecycles are used for testing. The proposed model is shown to predict the evolution of single-core convective cells effectively, including changes in 2D projected geometry and mean 2D and 3D reflectivity. In particular, by incorporating information on the vertical evolution of convective cores, the prediction errors of mean reflectivity (in both 2D and 3D) can be reduced by approximately 50% at 15-min forecast lead time, as compared to a persistence forecast. Keywords: radar, tracking, convective cell, nowcasting, 3D, deep learning, lstm.

What primarily regulates the evolution of convection in Hainan Island?

The evolution characteristics of convection in Hainan Island and their key regulators are still not fully recognized. Using years of high-resolution radar and reanalysis data, this study investigated the statistical characteristics of the evolution of convection in Hainan Island and discussed the key regulators leading to these characteristics. Results show that environmental winds significantly promote near-surface high potential temperature center and its induced convergence center to shift to the leeward side. The leeward-side convergence center is further enhanced due to the dynamical effects caused by the environmental winds bypassing or crossing the mountains, favoring convection to mostly occur on the leeward side in the late morning or at noon. After convection is triggered, it tends to develop northward in the afternoon due to northward migrations of the high potential temperature center and convergence center on the leeward side, and concentrate northward under southwesterly winds or contract southward under other environmental winds in the evening due to the decay of solar radiation heating and resultant divergence tendency. The findings can help to provide a basic prediction of the evolution of convection in Hainan Island.

Characteristics of Generating Cells in Wintertime Orographic Clouds

Abstract During the Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment (SNOWIE) field campaign, cloud-top generating cells were frequently observed in the very high-resolution W-band airborne cloud radar data. This study examines multiple flight segments from three SNOWIE cases that exhibited cloud-top generating cells structures, focusing on the in situ measurements inside and outside these cells to characterize the microphysics of these cells. The observed generating cells in these three cases occurred in cloud tops of −15° to −30°C, with and without overlying cloud layers, but always with shallow layers of atmospheric instability observed at cloud top. The results also indicate that liquid water content, vertical velocity, and drizzle and ice crystal concentrations are greater inside the generating cells compared to the adjacent portions of the cloud. The generating cells were predominantly <500 m in horizontal width and frequently exhibited drizzle drops coexisting with ice. The particle imagery indicates that ice particle habits included plates, columns, and rimed and irregular crystals, likely formed via primary ice nucleation mechanisms. Understanding the sources of natural ice formation is important to understanding precipitation formation in winter orographic clouds, and is especially relevant for clouds that may be targeted for glaciogenic cloud seeding as well as to improve model representation of these clouds. Significance Statement This study presents the characteristics of cloud-top generating cells in winter orographic clouds, and documents that fine-scale generating cells are ubiquitous in clouds over complex terrain in addition to having been observed in other types of clouds. The generating cells exhibited enhanced concentrations of larger drizzle and ice particles, which suggests the environments of these fine-scale features promote ice formation and growth. The source of ice formation in winter clouds is critical to understanding and modeling the precipitation formation process. Given the ubiquity of cloud-top generating cells in many types of clouds around the world, this study further motivates the need to investigate methods for representing subgrid-scale environments to improve ice formation in numerical models.

Temporal development of the displacement field of the Ponzano landslide in February 2017

The conducted research determined the temporal evolution of the displacement field for the Ponzano landslide case study. The offset-tracking method, so far used mainly for the relatively rapid but uniform displacement of glaciers, was tested for the 2017 study of the Ponzano landslide in Italy. The suitability of the method for high-resolution TerraSAR-X and medium-resolution Sentinel-1 imagery was investigated. The results proved the applicability of the OT method for studying processes with high and variable displacement dynamics. However, for such purposes, high-resolution radar data are crucial. With an uncertainty in the determination of residual displacements of about ±1 m, it was shown that the values of residual displacements occurring up to several days after the main phase of landslide movements are within the range of uncertainty but are determinable. The research conducted in the paper filled a gap in the analysis of the phenomenon just after the main movement phase. It allowed determination of the time and speed of extinction of landslide movements.

Long-time adaptive coherent detection of small targets in sea clutter by fast inversion algorithm of block tridiagonal speckle covariance matrices

Low-velocity small target detection in maritime surveillance radars is always a challenging task. Low signal-to-clutter ratio requires long-time coherent integration to obtain enough gain of target returns. However, long-time coherent integration encounters insufficient secondary data due to the spatial inhomogeneity of sea clutter. In this paper, considering the decorrelation time of speckle component of sea clutter short up to a dozen of milliseconds, the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and the inverse Gamma distributed texture is proposed to model sea clutter sequences in several tenths of a second. In this model, a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD) is constructed. Owing to the block tridiagonal structure of speckle covariance matrices, the adaptive detection requires much less reference cells for speckle covariance matrix estimation and much lower computational cost for its inversion. The proposed detector is verified by an X-band high-resolution island-based radar data with an unmanned aerial vehicle (UAV) as test target. The experimental result shows that it obtains competitive detection performance in comparison with the state-of-the-art detectors.

Spatial characterization of near-surface structure and meltwater runoff conditions across the Devon Ice Cap from dual-frequency radar reflectivity

Abstract. Melting and refreezing processes in the firn of the Devon Ice Cap control meltwater infiltration and runoff across the ice cap, but their full spatial extent and effect on near-surface structure is difficult to measure with surface-based traverses or existing satellite remote sensing. Here, we derive the coherent component of the near-surface return from airborne ice-penetrating radar surveys over the Devon Ice Cap, Canadian Arctic, to characterize firn containing centimeter- to meter-thick ice layers (i.e., ice slabs) formed from refrozen meltwater in firn. We assess the use of dual-frequency airborne ice-penetrating radar to characterize the spatial and vertical near-surface structure of the Devon Ice Cap by leveraging differences in range resolution of the radar systems. Comparison with reflectivities using a thin layer reflectivity model, informed by surface-based radar and firn core measurements, indicates that the coherent component is sensitive to the near-surface firn structure composed of quasi-specular ice and firn layers, limited by the bandwidth-constrained radar range resolution. Our results suggest that average ice slab thickness throughout the Devon Ice Cap percolation zone ranges from 4.2 to 5.6 m. This implies conditions that can enable lateral meltwater runoff and potentially contribute to the total surface runoff routed through supraglacial rivers down glacier. Together with the incoherent component of the surface return previously studied, our dual-frequency approach provides an alternative method for characterizing bulk firn properties, particularly where high-resolution radar data are not available.

Eddy Detection in the Marginal Ice Zone with Sentinel-1 Data Using YOLOv5

The automatic detection and analysis of ocean eddies in the marginal ice zone via remote sensing is a very challenging task but of critical importance for scientific applications and anthropogenic activities. Therefore, as one of the first steps toward the automation of the eddy detection process, we investigated the potential of applying YOLOv5, a deep convolutional neural network architecture, to specifically collected and labeled high-resolution synthetic aperture radar data for a very dynamic area over the Fram Strait. Our approach involved fine-tuning pre-trained YOLOv5 models on a sparse dataset and achieved accurate results with minimal training data. The performances of the models were evaluated using several metrics, and the best model was selected by visual examination. The experimental results obtained from the validation and test datasets consistently demonstrated the robustness and effectiveness of the chosen model to identify submesoscale and mesoscale eddies with different structures. Moreover, our work provides a foundation for automated eddy detection in the marginal ice zone using synthetic aperture radar imagery and contributes to advancing oceanography research.

Long lead-time radar rainfall nowcasting method incorporating atmospheric conditions using long short-term memory networks

High-resolution radar rainfall data have great potential for rainfall predictions up to 6 h ahead (nowcasting); however, conventional extrapolation approaches based on in-built physical assumptions yield poor performance at longer lead times (3–6 h), which limits their operational utility. Moreover, atmospheric factors in radar estimate errors are often ignored. This study proposed a radar rainfall nowcasting method that attempts to achieve accurate nowcasting of 6 h using long short-term memory (LSTM) networks. Atmospheric conditions were considered to reduce radar estimate errors. To build radar nowcasting models based on LSTM networks (LSTM-RN), approximately 11 years of radar, gauge rainfall, and atmospheric data from the UK were obtained. Compared with the models built on optical flow (OF-RN) and random forest (RF-RN), LSTM-RN had the lowest root-mean-square errors (RMSE), highest correlation coefficients (COR), and mean bias errors closest to 0. Furthermore, LSTM-RN showed a growing advantage at longer lead times, with the RMSE decreasing by 17.99% and 7.17% compared with that of OF-RN and RF-RN, respectively. The results also revealed a strong relationship between LSTM-RN performance and weather conditions. This study provides an effective solution for nowcasting radar rainfall at long lead times, which enhances the forecast value and supports practical utility.

Lightning Identification Method Based on Deep Learning

In this study, a deep learning method called Lightning-SN was developed and used for cloud-to-ground (CG) lightning identification. Based on artificial scenarios, this network model selects radar products that exhibit characteristic factors closely related to lightning. Advanced time of arrival and direction lightning positioning data were used as the labeling factors. The Lightning-SN model was constructed based on an encoder–decoder structure with 25 convolutional layers, five pooling layers, five upsampling layers, and a sigmoid activation function layer. Additionally, the maximum pooling index method was adopted in Lightning-SN to avoid characteristic boundary information loss in the pooling process. The gradient harmonizing mechanism was used as the loss function to improve the model performance. The evaluation results showed that the Lightning-SN improved the segmentation accuracy of the CG lightning location compared with the traditional threshold method, according to the 6-minute operating period of the current S-band Doppler radar, exhibiting a better performance in terms of lightning location identification based on high-resolution radar data. The model was applied to the Ningbo area of Zhejiang Province, China. It was applied to the lightning hazard prevention in the hazardous chemical park in Ningbo. The composite reflectivity and radial velocity were the two dominant factors, with a greater influence on the model performance than other factors.

Statistical characterization of rainfall fields based upon a 12-year high-resolution radar archive of Belgium

Spatial and temporal variability of rainfall input plays a critical role on the performance of the urban runoff simulations. For this reason, urban hydrological application demands accurate products of high-resolution and high-quality rainfall estimates, which use and understanding become critical for more robust models.In this study, the spatial and temporal characteristics of rainfall in Belgium are analyzed at fine scales. This is done by constructing a conceptual rain storm model using historical long-term radar data archive. This application involved the use of a state-of-the-art tracking algorithm, developed by Muñoz et al. (2018), to associate the spatial and temporal variations in rain storms and cells This algorithm enables better handling of high-resolution details and is capable of extracting rainfall objects/entities across various scales. Through tracking and conceptualizing these storm entities, spatial and temporal rainfall properties, such as geometric features, intensities, velocity, rainfall types or spatial patterns, can be extracted and analyzed. To accomplish this, twelve years of continuous high-resolution radar data are utilized in this work. The resulting analysis of the spatial and temporal characteristics of rainfall radar data is intended to set the ground for the future applications. More specifically, it can be used to refine and improve the existing long-term spatial rainfall generators, such as that the one implemented by Willems' in 2001.

Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed

Inclement weather and environmental factors impact traffic operations resulting in travel delays and a reduction in travel time reliability. Precipitation is an example of an environmental factor that affects travel conditions, including traffic speed. While Intelligent Transportation Systems services aim to proactively mitigate congestion on roadways, these services are often not sensitive to weather conditions. This paper investigates the application of high-resolution weather data in improving the performance of proactive transportation management models and proposes short-term speed prediction models that fuse real-time high-resolution weather surveillance radar data with traffic stream data to conduct spatial and temporal prediction of the speed of roadway segments. Extreme gradient boosting weather-aware speed prediction models were developed for a 7-km segment of Interstate 270 in St. Louis, MO, USA. The performance of the weather-aware models was compared with the performance of weather-insensitive speed prediction models that did not take precipitation into account. The results indicated that in the majority of instances, the weather-aware models outperformed the weather-insensitive models. The extreme gradient boosting models were compared with the K-nearest neighbors algorithm and feed-forward neural network models. The extreme gradient boosting model consistently outperformed the other two methods. In addition to speed prediction models, van Aerde speed-flow traffic stream models were developed for rain and no-rain conditions to study the impact of precipitation on the traffic stream across the corridor. Results indicated that the impact of precipitation is not identical across the corridor, which was mirrored in the results obtained from weather-aware speed prediction models.

1-km soil moisture retrieval using multi-temporal dual-channel SAR data from Sentinel-1 A/B satellites in a semi-arid watershed

Soil moisture is an important variable in agricultural and hydrological applications. Sentinel-1 (A/B) satellite provides high-resolution (∼10 m) synthetic aperture radar data, and its high revisit capability (6 days at the equator) with multi-track data provides great potential for global soil moisture monitoring. In this study, an algorithm was proposed for soil moisture retrieval using the Multi-Temporal Dual-Channel (MTDC) Sentinel-1 data. The MTDC algorithm first conducted an incidence angle normalization method based on time series data to normalize backscattering coefficients from multiple tracks. Subsequently, using the vegetation water content obtained through a vegetation index climatology, soil roughness and soil unfrozen water content could be estimated for the freezing period (unfrozen water content in the soil is assumed to be stable for January and December). The changes in total backscatter between successive observations were expressed as a function of the two-way vegetation transmissivity multiplied by the difference between soil backscatter, which was directly related to the changes in soil moisture. Considering the retrieved soil roughness as a constant for the entire year, and the average unfrozen water content as the initial soil moisture, the changes in and absolute values of soil moisture for each observation can be estimated by minimizing a cost function between the modeled and observed changes in total backscatter. Retrieval results were validated against the soil moisture network in a semi-arid watershed of Shandian River with an unbiased Root Mean Square Error (ubRMSE) of 0.06 cm3·cm−3, and a bias of −0.04 cm3·cm−3. With the initial soil moisture from in-situ measurements, a higher accuracy was achieved for the MTDC algorithm (ubRMSE = 0.06 cm3·cm−3; bias = −0.01 cm3·cm−3). Further, comparisons with Soil Moisture Active Passive (SMAP) enhanced products further verified the efficacy of the proposed algorithm (ubRMSE = 0.03 cm3·cm−3; bias = −0.004 cm3·cm−3), and demonstrated the feasibility of operational soil moisture products using the Sentinel-1 radar dataset.

Additional Evaluation of the Spatiotemporal Evolution of Rotation during Tornadogenesis Using Rapid-Scan Mobile Radar Observations

Abstract This study builds upon recent rapid-scan radar observations of mesocyclonic tornadogenesis in supercells by investigating the formation of seven tornadoes (four from a single cyclic supercell), most of which include samples at heights < 100 m above radar level. The spatiotemporal evolution of the tornadic vortex signatures (TVSs), maximum velocity differentials across the vortex couplet, and pseudovorticity are analyzed. In general, the tornadoes formed following a non-descending pattern of evolution, although one case was descending over time scales O(<60) s and the evolution of another case was dependent upon the criteria used to define a tornado, and may have been associated with a rapidly occurring top-down process. Thus, it was determined that the vertical sense of evolution of a tornado can be sensitive to the criteria employed to define a TVS. Furthermore, multiple instances were found in which TVSs terminated at heights below 1.5 km, although vertical sampling above this height was often limited. Significance Statement It is generally well understood that tornadoes form over short time scales [i.e., O(∼60) s]. Despite this fact, detailed scientific measurements of tornado evolution during and just prior to genesis remains limited, particularly very near the ground and on time and space scales sufficient to observe tornado processes. Multiple recent studies have supported a non-descending evolution of rotation in supercell tornadoes, but the small number of analyzed cases is still insufficient for generalization. This study investigates seven new cases of tornadogenesis using high spatiotemporal resolution radar data that include near-ground level observations to examine the evolution of rotation with time and height. For the time scales observable by the radar platform [i.e., O(∼30) s], genesis occurred predominately following a non-descending manner in five out of the seven tornadoes studied, while the vertical evolution of two tornadoes were sensitive to the criterion used to define a “tornadic” vortex signature.

An innovative approach for retrieval of gridded urban canopy parameters using very high resolution optical satellite stereo

ABSTRACT Paucity of information on Urban Canopy Parameters (UCPs) is considered as one of the major reason behind limited urban climate research and modelling in developing region of Asia, Africa, and Latin America. UCPs define those characteristics of urban built form which have direct or indirect influence on urban climate. Most of the studies in developed world have utilized 3-Dimension (3D) Geographic Information System (GIS) database either developed from ground survey or Remotely Sensed (RS) data such as Aerial Photographs, Airborne Light Detection and Ranging (LiDAR) and high-resolution Interferometric Synthetic Aperture Radar (InSAR) data for retrieval of UCPs. However, non-existence of 3D GIS database and limited availability of above RS datasets in developing region necessitates to employ widely available low-cost alternative datasets for retrieval of UCPs. Hence, this study focuses on retrieval of UCPs by employing Very High Resolution Satellite (VHRS) optical stereo data, which has repeat availability, low cost and extensive coverage, in a highly dense and complex urban environment with a challenging composite climate like Delhi, India. A novel methodology for extraction of gridded UCPs from VHRS optical stereo data has been developed in this study to overcome the limitation of extraction of individual building footprint in dense and compact urban built-up. The validation of key UCPs such as building height (Mean error, Root Mean Square Error (RMSE) and Mean Absolute Error <1 m in all height groups), Building Surface Area (Accuracy 84.27%) and Sky View Factor (RMSE = 0.046 and correlation = 0.94) with ground measurements has displayed reasonable accuracies. Hence, the study demonstrates successfully the use of VHRS optical satellite stereo data for generation of gridded UCPs in a highly heterogeneous and dense urban built-up environment. The developed approach is globally replicable in any city of any country.

On the Discovery of a Roman Fortified Site in Gafsa, Southern Tunisia, Based on High-Resolution X-Band Satellite Radar Data

The increasing availability of multiplatform, multiband, very-high-resolution (VHR) satellite synthetic aperture radar (SAR) data has attracted the attention of a growing number of scientists and archeologists. In particular, over the last two decades, archeological research has benefited from SAR development mainly due to its unique ability to acquire scenes both at night and during the day under all weather conditions, its penetration capability, and the provided polarimetric and interferometric information. This paper explored the potential of a novel method (nonlocal (NL)-SAR) using TerraSAR-X (TSX) and Constellation of Small Satellites for Mediterranean Basin Observation (COSMO)-SkyMed (CSK) data to detect buried archeological remains in steep, rugged terrain. In this investigation, two test sites were selected in southern Tunisia, home to some of the most valuable and well-preserved limes from the Roman Empire. To enhance the subtle signals linked to archeological features, the speckle noise introduced into SAR data by the environment and SAR system must be mitigated. Accordingly, the NL-SAR method was applied to SAR data pertaining to these two significant test sites. Overall, the investigation (i) revealed a fortified settlement from the Roman Empire and (ii) identified an unknown urban area abandoned during this period via a field survey, thus successfully confirming the capability of SAR data to reveal unknown, concealed archeological sites, even in areas with a complex topography.

ColoRadar: The direct 3D millimeter wave radar dataset

This work presents two different forms of dense, high-resolution radar data from two frequency modulated continuous wave radar sensors, along sparse radar pointclouds produced by one of the radar sensors. In addition, all datasets include 3D lidar and inertial measurements, and a lidar-based simultaneous localization and mapping pose estimation. Over 2 h of 6D pose data was generated across 52 datasets collected in highly diverse 3D environments including lab spaces, outside and inside large buildings, urban walkways, and a mine. One dataset, from the ASPEN Lab, also includes precision groundtruth generated from a motion capture system. Intrinsic radar calibration and measured extrinsic sensor position calibrations are also provided along with python based development tools to interact with the various datasets. This data is designed to assist with generating radar based localization algorithms and calibrations between radar and other sensors.