dataesr/scientific-paragraphs-categorization huggingface dataset:
Only five layers have been found in the Socioeconomic data category probably because normally this data refers to alphanumeric information with no spatial representation (Fig. 5 ). The figure  5  shows that in the inventory lots of datasets are available in Web Services but also that some particularly relevant data are available only in shapefile format and the most represented format is the WMS that provides only a visualization of the spatial data. The low number of local datasets presented in this inventory is due to the selection of datasets covering the whole area to provide an overview. Local datasets can be of course useful for particular and local MSP implementation, depending on the defined scale specifically at local hot points, but their identification and collection remain difficult because they are available from different sources and they are not centralized.
As part of this analysis, a number of measures addressing data needs and gaps have been identified in order to improve MSP data sharing among countries. Some of them depend mainly on the data producers and on the cooperation framework. The resulting recommendations are gathered in an Action Plan (Table  1 ) and they are focused on improving the portrayal, interoperability of metadata and Web Services. The goal of the SDP was to provide an effective tool displaying continuous spatial information of the transboundary area for stakeholders and planners facilitating the decision-making process. To evaluate the functionality of the portal, it was launched open for stakeholders to test it. Additionally, GIS experts or data experts were invited to experiment datasets interoperability and to address needs and gaps and possible solutions to overcome them in support of the "Data and Information Requirements for MSP".
Stakeholders data analysis: Potential approaches for stakeholder engagement on MSP The approach adopted for stakeholder engagement in SIMNORAT consisted on the organization of multisectors participatory workshops, based on information collected during interviews of different stakeholders from the three countries. The aim was to involve stakeholders in cross-border discussions and to support the sharing and use of good practices for the stakeholder engagement process, in order to contribute to the better coherence of the MSP processes in the three countries. For the interviews phase, once the list of potential stakeholders to be interviewed was settled, the best method to contact them was by email and by phone call. A call is important in order to have direct contact with them and also to explain what the objective of the interview will be and how it fits into a specific process.
Case Studies The SIMNORAT project includes two case studies in cross-border areas dedicated one to the cumulative effects assessment of anthropogenic pressures on the marine environment between SP and FR, in the Bay of Biscay. This case study aimed to explore tools, methods and data to assess environmental effects of maritime uses in the context of MSP and transboundary issues. The other case study shared by PT and SP, supported a conceptual methodology to create and manage a cross-border Marine Protected Area (MPA) between both countries. In order to achieve this, the case study focused on the existing Spanish MPA of Galicia Bank and on the Vigo and Vasco da Gama Seamounts.
The method used was the "CARPEDIEM" for CEA based on one matrix establishing relationships between human activities and pressures and another one matrix describing the ecological sensitivity of habitats to different pressures (Figure  6 ). Finally, some challenges were identified in the development of this component. Identification and collection of existing / available data in both countries was very time consuming and not totally successful. Data was not available for the same periods of time in both countries or it was not possible to exchange. Therefore, data quality control and raw data edition to generate standardized and comparable datasets was a hard task to accomplish. Consequently, there was a need to assess differences based on application of this different data. Also, this task laid the foundations for a relationship between project partners, a necessary first step in order to develop coherent planning and management approaches on both sides of the maritime borders  63.
On the other hand, the ultimate goal of this task was to produce diagnosis useful to managers and decision-makers involved in MSP. Maps produced for this component are good illustrations of cross-border issues for conservation of marine biodiversity, showing that these methodologies play a key role in delivering EBM approaches into MSP  63. Figure  7. Intensity of multi-pressures that can interact with great grey gulls (simulation with Spanish and French datasets). The main challenge faced by this case study was the collection and production of coherent datasets from both countries (e. i. in figure  7 ). However, the main interesting result was the networking of Spanish and French teams and the technical and methodological developments. Moreover, on the technical aspect, the pelagic component had not been studied yet in the cumulative effects assessment tool CARPEDIEM and progress could be achieved.
The analysis implied the compilation of administrative, ecological, geological and maritime activities data in order to identify main challenges in transboundary MPA planning (jurisdiction disputes, accessing international data. ), identification of knowledge gaps and synergies, conflicts between conservation and maritime activities, and stakeholder engagement in the case study area. In addition, the analysis of the governance framework in Spain and Portugal regarding marine conservation and MSP and the comparative analysis of Portuguese and Spanish maritime and coastal planning policies and management tools, was crucial to complete the case study. This work evidenced there are differences in governance frameworks between Spain and Portugal concerning MSP and nature conservation, namely marine conservation responsibilities.
b) Management scale, an integral part of the planning boundaries, related to the definition of focus areas where operational objectives will be developed, depending on the peculiarities and the characteristics of each local area. In this sense, the case study of the Cross-border MPA between Spain and Portugal fits the specific objectives of SIMNORAT project as a step forward in understanding current and potential future demands relevant to transboundary conservation areas, access to data and data-specific barriers to transboundary cooperation. Additionally, it considers potential options for transboundary cooperation in a context of a cross-border marine protected area including marine EBM approach  55. Regarding spatial data, there is a general agreement on the fact that MSP should be based on the best available, high quality and up-to-date spatial data  59, 60.
The SPDP developed, was structured with a focus on INSPIRE web services (WMS, WFS, and WMTS). This brings several benefits as data is stored by the producer which avoids unnecessary duplication and lowers the administration processes. This protocol guarantees an access to the most up-to-date published data, not to duplicate the data maintenance work and it does not require storage of the information. Some opportunities have been found when defining scale and boundaries according to EBM, as the availability of some public databases regarding geology, habitats and oceanographic variables at supranational scale (e. g. EMODNET). However, there are still gaps in some types of data and when considering the definition of the "ecological boundaries" in the dynamic nature of some important ecological items (i.
Acknowledgements This work was supported by  SIMNORAT EASME/EMFF/2018/1. 2. 1. 5/SI2. 806423 ; SIMATLANTIC EASME/EMFF/2015/1. 2. 1. 3/03/SI2. 742089 ; CESAM (UID/AMB/50017/2019 ); FCT/MCTES through national funds, and the co-funding by the  FEDER , within the  PT2020 Partnership Agreement and Compete 2020. We wish to express our thanks to all other members of the partner organizations involved in the project including AZTI and CETMAR. We would like to thank the reviewers for their valuable comments on the manuscript.
The performance of five open-source methods Vins-Mono, ROVIO, ORB-SLAM2, DSO, and LSD-SLAM is compared using the EuRoC MAV dataset and a new visual-inertial dataset corresponding to urban pedestrian navigation. A detailed analysis of the computation results identifies the strengths and weaknesses for each method. Globally, ORB-SLAM2 appears to be the most promising algorithm to address the challenges of urban pedestrian navigation, tested with two datasets.
Visual SLAM (vSLAM) using solely cameras and visual-inertial SLAM (viSLAM) using inertial measurement units (IMUs) give a good illustration of these new SLAM strategies. vSLAM has probably attracted most of the research over the last decades. Cameras capture numerous data about the observed environment that can be extracted and used for SLAM processing. These cameras are also among the cheapest sensors. Their presence on most of the smart devices today supports the ongoing development of novel applications that target the general public. Because there exist many different SLAM methods targeting different objectives, comparing them is not easy. Choosing the best-suited method for a specific application requires a good knowledge of the ins and outs of SLAM as well as a global understanding of state-of-the-art SLAM strategies.
The performance of a method depends on the application context and the challenges to be addressed. Globally, SLAM tends to be wrongly considered as an almighty technology but real-life implementation raises many issues in terms of computational limitations, noise mitigation, or even user-friendliness. This is only a selection of the difficulties to overcome. This paper is aimed at classifying existing vSLAM and viSLAM methods. The proposed transverse classification is based on technical and application-oriented criteria. The review of SLAM methods continues with a historical presentation of vSLAM and viSLAM development. The analysis is completed by running five selected state-of-the-art SLAM methods, which have been chosen to represent the diversity of existing SLAM designs, on two different datasets.
These methods best address the use case of pedestrian pose estimation in the urban environment. This experimental benchmark is conducted on a renowned public dataset EuRoC  [6]  and completed with a new visual-inertial dataset, which has been recorded with smart devices held in hand by a pedestrian in the city center of Nantes in France (IRSTV dataset). The main motivation supporting this review and benchmark is to assess vSLAM and viSLAM methods in the specific context of pedestrian mobility in the city with augmented reality (AR) used along the journey. Its outcomes should ease the choice of the most suitable methods to estimate the pose of a handheld smart device in this context. Pedestrians' hands are performing 6DoF motions. Looking for popular benchmarking datasets, it was found that these movements are similar to those of micro air vehicles.
The paper starts with a classification of the methods derived from the literature according to their characteristics and their robustness to various scenarios (Section 6) to select a dataset. The closest ones to the pedestrian mobility requirements and context were chosen for comparison with the newly introduced IRSTV pedestrian dataset (Section 7). Section 2 lists existing surveys and benchmarks of SLAM methods with different approaches than the one adopted in this work. Section 3 describes the first level of SLAM algorithms' design: hardware and general software choices. Section 4 describes the general architecture of the vSLAM algorithm. It identifies and details four constitutive "blocks. " Section 5 gives an overview of the SLAM history, which is divided into three ages. Section 6 presents a new classification of vSLAM and viSLAM methods.
Existing Surveys and Benchmarks Several survey papers present a snapshot of the state of research on SLAM at a given time. Various papers present other experimental benchmarks. They inform about the performance of the most famous SLAM methods on the given dataset. This section lists some of these papers to conduct a theoretical benchmark. They are also listed to support the comparison of the experimental benchmark, conducted in Section 7, with other state-of-the-art assessments. 2. 1. Survey Papers. An interesting and complete, albeit a little old, review on the vSLAM algorithm can be found in  [7]. In  [8] , the authors introduce some of the main differences between state-of-the-art SLAM methods as well as the most famous algorithms in vSLAM, with a very useful introduction to viSLAM. However, as a survey, it mainly provides high-level explanations on the subject.
An overview limited to visual odometry and visual SLAM can be found in  [9]. Two founding papers to understand the origin of SLAM research are in  [10, 11]. They also mainly concentrate on visual odometry with a subpart on viSLAM. The 2006 papers by Durrant-Whyte and Bailey  [12, 13]  provide rich tutorials on viSLAM. They contain educational and detailed presentations of the mathematical formulation of the SLAM problem but are lacking an updated presentation of recent vSLAM. The paper by Cadena et al. [14]  can be considered as a handbook in the field of viSLAM. A recent review  [15]  lists and classifies filtered-based and optimization-based viSLAM algorithms and compares them using the EuRoc dataset. It is found that none of these papers provides a complete presentation of the subject, i.
Benchmarks. A first benchmark  [16]  focuses on RGB-D SLAM only. Another benchmark  [3] , dedicated to visualinertial methods, evaluates tightly coupled Visual Inertial Odometry (VIO) and viSLAM methods on multiple platforms to simulate real-life applications with flying drones. The tests are only performed on the EuRoC dataset (i. e. , medium indoor environment). It provides one of the most complete benchmarks on viSLAM algorithms by comparing the accuracy, memory and CPU usage, and computation time of six state-of-the-art algorithms. The S-MSCKF paper  [17]  compares some viSLAM methods as well. vSLAM methods are often tested on the new college  [18] , TUM monocular  [19] , or TUM RGB-D dataset  [16]  that do not include inertial data. This dataset cannot be used to compare the latest viSLAM methods.
But these systems use a few of the last measurements to estimate the pose in real time. To use previous optimizations and reduce computation, incremental solvers, such as  [47] , solve a nonlinear estimation problem in a real-time approach. They update the estimated model of the environment every time a new measurement is added using the sparse structure of the underlying factor graph. Loop closing is a two-step process. Firstly, it starts with the loop detection, also called place recognition. The place recognition process can be used to solve the problem of track loss recovery, generally by using the loop closing thread. Most of the methods use a bag-of-word approach to compare new keyframes with a database of previously acquired views. "Bag-of-word" refers to the set of descriptors that identifies patches in images as in the DBoW2 method proposed by  [48]  based on FAST  [31]  and a slightly modified version of BRIEF features  [49].
A catalog search of similar words between the frame and the database is extremely quick and efficient. Once a potential similarity is found, multiple verification steps verify if it corresponds to a loop. Secondly, the loop closure corrects the map and poses. The transformation between the two views is computed and 3D points are fused. The computations needed to close the loop are then distributed along the entire pose graph and the map using (local) BA. The loop closing process can be computationally heavy. It is generally done in a dedicated thread.
Journal of Sensors Open Keyframe-Based Visual Inertial System (OKVIS)  [73]  and S-MSCKF  [17]  are famous stereo VIO methods, while Vins-Mono  [74]  is a real viSLAM and not just a VIO method. Kimera  [60]  is also based on a VIO method but it also includes a pose graph optimizer, in different threads, for global trajectory estimation, a 3D mesh reconstruction module, and a 3D metric-semantic reconstruction module. VIORB  [75]  is based on ORB-SLAM  [76]. Its front-end extracts feature with ORB while its back-end runs graph optimization. But its main interest lies in a new IMU initialization method that first estimates the gyroscope's bias, approximates the scale and the gravity (without considering accelerometer bias), and then estimates the accelerometer bias (with scale and gravity direction refinement) and finally the velocity vector.
They were also considered by  [3]  as the most accurate and robust algorithms across all platforms and datasets, and ROVIO is considered as a good compromise. Chen et al. [15]  also consider Vins-Mono as the algorithm with the best accuracy among the viSLAM algorithms under test. Not only were these algorithms selected based on their importance in the field but also on the requirements of the use case of interest: pedestrian's pose estimation with a handheld device in urban environments. Selected methods should target accurate pose estimation in a challenging context and be available in open source. No restriction linked to computational difficulties was applied for this benchmark. Global robustness to many perturbations is expected as we seek an implementation on a handheld device, such as a smartphone.
We only selected methods that work with a monocular camera and IMU for the viSLAM part. Pedestrian applications mean that the user's behavior has to be considered. The movement of a handheld camera can be shaky, and fast uneven motion will occur. In terms of user requirements, we preferred methods that did not require a very specific manual initialization. As most of the pedestrian applications are being outdoors (e. g. , in urban environments), the selected algorithms must handle large-scale scenarios, long experiments (several minutes to an hour), and light changes. Kimera  [60]  and VIORB  [75]  could have been added to this benchmark considering their assessment in the above classification. But even if Kimera aims at running with monocular camera, one part of the code (the loop closure detection) requires a stereo camera at the moment.
Experiments and Dataset Features. The following features were adopted for the experimental setup, data acquisition, and analysis. They are specific to our use case. (vii) General public: user-friendliness is expected (no specific manual initialization, no need to adjust parameters to each scenario) The EuRoC MAV dataset  [6] , including inertial data, was chosen for the overall comparison of all five methods. Other IMU+vision datasets could have been considered to extend our experimental assessment. In  [83] , an indoor/outdoor dataset is described but its ground truth accuracy is only 15 cm as compared to the 1 mm with the EuRoc dataset. The dataset presented in  [84]  is recorded by a MAV in Zurich urban space. The ground truth of the MAV displacement is postprocessed using Pix4D photogrammetry mapping instead of being surveyed by an external localization system (e.
g. , a motion capture system) as it is in the EuRoc dataset. The dataset presented in  [85]  is another interesting candidate with good accuracy and higher test sequence variability than EuRoc. We choose EuRoc since its broad adoption in other reviews eased the comparison of our results with other studies. We will consider other datasets in future work. Interested readers can find other datasets referenced in  [15, [83] [84] [85]. EuRoc comprises various scenarios of drone flight in the same environment and six degrees of freedom ground truth. We choose to focus only on three scenarios among the 11 available. They correspond to different characteristics, detailed in Table  4 , which are all interesting to assess the performance of the selected algorithms. Let us note that the drone flight movements show similarities with those of a handheld camera, contrary to the car-embedded cameras of the KITTI dataset  [86] , whose 10 Hz sampling frequency is not sufficient to track rapid movements.
The amplitude of hand movement is often underestimated. Movements are more important than those of the arm naturally synchronized with the walking gait. The handheld device is turned over and raised to explore the environment. This is why we can compare it to the drone dynamics. The selected methods were also tested on a new dataset called "IRSTV" collected by a pedestrian walking with a handheld device along the urban   6 , along with the hardware setup. It corresponds to a 466 m walking path in both indoor and bright outdoor spaces. It comprises images with scenes at different scales: streets, open spaces, and rooms. It also contains observations of glass-covered buildings that reflect the environment, which classically fails visual odometry. No specific motion for dynamic initialization was imposed at the beginning of the acquisition.
It provides the ground truth for the pedestrian's scenario with 0. 3% horizontal positioning accuracy of the cumulative walking distance. It is shown in green in Figure  6. The benchmarking is conducted on a 2. 60 GHz Intel Core i7-6700HQ CPU. Our Linux environment is in a virtual machine (Oracle VirtualBox 5. 2. 12). We allowed all four cores with 100% allocated resources and 5 GB of RAM usage. All algorithms are tested in Ubuntu 16. 04. When ROS was required, we used ROS kinetic with a catkin workspace. The next section presents a detailed analysis of the chosen algorithms on the selected dataset and assesses the most suitable for our case.
Experimental Assessment Methodology. We ran the tests on the two previously presented datasets starting with EuRoc dataset. All results are averaged on five runs to account for random outlier mitigation parts. In Tables  5  and 6 , we provide the mean values of the Relative Positioning Error (RPE) and the Absolute Positioning Error (APE) for each method on each dataset. APE and RPE wre obtained with obtained with evo package github. com/MichaelGrupp/evo. APE is the Euclidean distance between the estimated position (2D or 3D) and the true position whereas RPE is the     14.
It can also mean that major "singular" errors are likely to happen locally and are not corrected. In this case, the max RPE should be much bigger than the RMSE RPE. Using the provided timestamps, we compared the real position with the estimated position to compute APE. They are computed in 3D for the EuRoc dataset but only in 2D for the IRSTV dataset since PERSY only provides the ground truth of the pedestrian's path in the horizontal plane. The cumulative APE distribution functions (CDF) are plotted for global accuracy and stability assessment of the tested algorithms. The errors are computed in the local navigation frame defined by the x and y axes forming the local horizontal plane and the z axis pointing upward. To simulate real-life applications where no human intervention is needed between various tasks in diverse environments, the parameters needed to run the computation were fixed for all runs.
To conclude, ORB-SLAM2 is found to be consistent and robust in the various tested scenarios (including very large scales) and has very reliable optimization and loop closing frameworks. It performs at least as great as other state-ofthe-art methods including Vins-Mono despite using only the visual information of a high-speed visual-inertial dataset. Its visual odometry base is very sensitive to lacking texture, but loop closure and relocalization allow to correct the trajectory. However, let us notice that ORB-SLAM2 has a very restrictive keyframe-culling policy, which means that it also outputs fewer poses. This obviously improves the output compared to what is truly estimated. It also means that the results sometimes give several seconds without any pose output because no new keyframe was captured during this time (or it was later culled out) as seen in Figure  8.