dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to choose the correct road when two roads are close to each other and have a similar direction, the probabilistic criterion is calculated. Three methods for calculating this criterion were proposed depending on the availability of sensors on the vehicle. We also estimated the lanes numbers by extracting the information about the number of lanes from the OSM database. To improve the results, a heuristic about the change in lanes number has been used. In this end, we were able to get up to a 83. 64% correct rate estimation for a set of 6596 GPS points. We plan to use this map matching algorithm on other maps in order to compare the results. In order to do that, we intend to implement a unique criterion for the map matching, which uses all the three criterion presented in this paper.
We also plan to implement our algorithm in real time. Indeed, our algorithm was performed on the integral data set under Matlab for a computation time of 12 minutes and 30 sec on a intel core i7-6820HQ CPU 2. 70 GHz. Furthermore, we conclude that getting the number of lanes using only OSM is not sufficient. The information about the number of lanes is tributary to selected region and the contribution of volunteers on this region. Therefore, for our future work we want to use the information about the number of lanes as prior for a information driven lanes recognition process using a visionbased sensors  [19].
CONCLUSION AND PERSPECTIVES This study case allows us to test the first part of our methodology, by adapting a BIM model thanks to building project actors' needs, in this case the end user needs. This VR prototype has to be tested by the architect and in a project review meeting, to evaluate and improve it thanks to the user feedback. Notice that we Then some researches have to be done on BIM data transfer, VR system choices and particularly on the smart data filtering, which could be linked to the planned virtual walkthrough. Finally, we would search about how to improve the preparation ways for creating these models, trying to automatize or simplify processes.
For co-polarized waves, scattering from the ground plays a large role particularly when the vegetation layer is removed, which might hamper the retrieval of disturbed areas. The aim of this study was to evaluate the sensitivity of L-band data to changes in forest structure caused by wind and insect outbreaks. The specific objectives were i) to evaluate the extent to which changes in forest structure caused by wind and bark beetle outbreaks influence the L-band SAR signal, ii) to understand the influence of preand post-disturbance SAR acquisition timing as well as of post-disturbance management practices on SAR backscatter changes, and iii) to estimate the accuracy of disturbance mapping using L-band SAR data.
This subset forms the reference vector layer for insect outbreaks. The minimum limit of 0. 1 ha was used as it roughly matches the spatial resolution (30 m) of the processed SAR datasets. Locations of trees toppled by the storm Kyrill were digitized on the DMC image acquired on September 16, 2007 and saved as a vector file  (Heurich et al. , 2010b). The vector layer contains information on the affected areas and management interventions (clearing vs. no intervention) after the storm. A subset of this vector layer (423 polygons with an area between 0. 1 and 10 ha) was used. This subset forms the reference vector layer for windthrows. These reference vector layers were used to extract samples (i. e. , pixels) for all subsequent analyses. The analysis was conditioned by the availability of SAR data sets because acquisitions from the same satellite orbit are needed before and after disturbance to allow for pixel-based comparisons.
Only advanced land-observing satellite (ALOS) phased array-type L-band synthetic aperture radar (PALSAR), fine beam dual (FBD), and polarimetric (PLR) acquisition modes were considered due to their high spatial resolution and the availability of the cross-polarized (HV) channel. High spatial resolution was needed to account for spatial heterogeneity of disturbances. The cross-polarized channel was deemed necessary based on earlier results that showed its sensitivity to forest structural characteristics and their change  (Joshi et al. , 2015; Mermoz and Le Toan, 2016; Tanase et al. , 2015b; Tanase et al. , 2014). SAR data were provided by the Japanese Aerospace Exploration Agency (JAXA) in singlelook complex (SLC) format. Four PLR images were used to derive change ratios (post-event divided by pre-event backscatter values) for dates corresponding to the windstorm Kyrill.
The effects of insect outbreaks were evaluated using 13 dual-polarized (HH, HV) FBD datasets available from two orbital paths (i. e. , 635 and 636). The datasets were acquired with an incidence angle at swath center of 24°and 39°for PLR and FBD modes, respectively. The acquisition dates together with the environmental conditions at acquisition are presented in Table  1. Information on accumulated precipitations aided in the interpretation of the results. Accumulated precipitation provides an indication of the environmental conditions at image acquisition; datasets acquired during stable, dry periods showed improved sensitivity to forest structure in a range of environments from Mediterranean to boreal  (Cartus et al. , 2012; Kalogirou et al. , 2014; Lucas et al. , 2010; Tanase et al. , 2010).
SAR data processing ALOS PALSAR data acquired on the same orbital path were co-registered using as reference the first image of each data series and a cross-correlation algorithm  (Werner et al. , 2005). After co-registration, each image was multi-looked in range and azimuth to obtain a ground pixel spacing of approximately 30 m. The high resolution was necessary (out of total) reported for the acquisition date (i. e. , includes precipitations from 9 am on the previous day to 9 am on acquisition day) **Precipitations (of total) reported for the following day after image acquisition (i. e. , includes precipitations from 9 am on the acquisition day to 9 am the following day; the actual image acquisition time falls within this interval). to allow preservation of the localized influence of disturbance events and thus to avoid errors in SAR signature analysis.
Windthrow analysis SAR values for each polarization (i. e. , σ ⁰ HH , σ ⁰ HV , σ ⁰ VV ) and acquisition date were extracted for pixels located within the polygons digitized as affected by the storm Kyrill. Ten large polygons were added to the reference vector layer through the digitization of areas seemingly not disturbed over the past years. Such areas were identified using recent optical imagery (Google Earth and Bing Aerial layers) and were used to estimate backscatter change in undisturbed forests. A total of 6053 pixels (2725 affected by wind and 3328 not affected) were extracted and used for statistical analyses of the backscatter trends and to compute the threshold values separating affected areas from stable, unaffected ones. RCR values were computed for each polarization using the single pre-event dataset available.
For each RCR, the mean, median, standard deviation, and CI (95%) were estimated. The dynamic range, i. e. , difference between average pixel backscatter of disturbed and undisturbed areas, was used as an indicator of index sensitivity to forest structural changes  (Cartus et al. , 2012). The mean and median RCR values of areas affected by wind were compared to those of unaffected areas. Furthermore, the mean (and CIs) and median values were analyzed as a function of the post-event management interventions (no intervention vs. salvage logging) and environmental conditions at image acquisition. Based on the observed trends, average values, CIs, and the dynamic range, thematic maps showing windthrows location were derived. The maps were based on thresholds on the HV backscatter change for the datasets acquired in April 2007 and 2011, relative to the dataset acquired before the storm (0.
6 dB and -1. 8 dB, respectively). The thresholds were derived by averaging median values observed for affected and unaffected areas. Derived maps were validated using an independent dataset. The independent dataset was obtained by randomly selecting pixels affected by wind out of an initial set of pixels falling within the boundaries of the reference vector layer. The sampling size for validation was computed according to Eq. (  2 )  (Olofsson et al. , 2014). A stratified random sampling design was selected; the number of samples for the disturbed strata was computed according to Eq. (  3 ); the number of samples for the undisturbed strata were obtained as the difference between the total number of samples and the samples of the disturbed strata. = ∑ + ∑ n w S S O N w S ( ) [ ( )] (1/ ) i i i i 2 2 2 (2) where S(O) is the expected standard error for the overall accuracy, W i is the class proportion, S i is the standard deviation of stratum i (as de- scribed by = - Si U U (1 ) , i i where U i is the expected user accuracy), and N is the sampling population.
Insect outbreak analysis Areas affected by insect-related disturbances were matched with ALOS FBD polarized (σ ⁰ HH , σ ⁰ HV ) datasets according to the outbreak detection date (ODD) and the post-event SAR image acquisition date. The ODD for the three years analyzed was  August 31, 2008; August 20, 2009; and August 22, 2010. As for windthrows, the analysis focused on the statistical properties of areas affected by beetle outbreaks compared to those of unaffected areas. The number of pixels used for analyses is presented in Table  2. For validation purposes, sets of random pixels were selected by year of disturbance from the pool of all affected pixels. The validation datasets were generated for each year between 2007 and 2010. The number of validation samples was computed as described in the previous section.
e. , green, red, gray). To limit the influence of variable forest conditions, caused by the continuous nature of the outbreaks and human interventions, the postevent means were computed over short periods (e. g. one summer) when only few images (up to three) were available (Table  1 ). Datasets acquired under extremely wet conditions (2009. 07. 01) or at freezing temperatures  (2009. 05. 15)  were removed from the analysis as it has been demonstrated that such events might diminish SAR sensitivity to forest structure  (Cartus et al. , 2012; Lucas et al. , 2010; Santoro et al. , 2002; Tanase et al. , 2010; Thiel et al. , 2009). Another factor considered when deciding which images are useful for temporal averaging was the acquisition date relative to the bark beetle phenology. For example, when analyzing the 2008 event (using 2009 post-event data), one needs to exclude from pre-event averaging the image acquired in July (2008.
e. , salvage logging was carried out after the satellite pass). In such cases, it was considered that at SAR image acquisition, trees were still present on site. Table  3  provides information relating postevent images to the analyzed polygons as a function of outbreak detection date. As pre-event image, all datasets acquired before the detection year and their average (except for images acquired under heavy rainfall and freezing conditions) were used. The pixels used for statistical analysis were extracted only if located within the affected polygon boundaries to limit the mixing of different forest conditions (affected vs. unaffected). Ten polygons were added through digitization of areas seemingly not disturbed over the past years. The number of pixels available for the analysis depended on the post-event image acquisition date, as areas marked as 'cleared' might have been excluded for some years (Table  3 ).
Windthrows As expected, lower RCR values were observed over wind-affected areas than over unaffected areas at all polarizations. This pattern was the result of decreased post-disturbance backscatter levels over affected areas. A decrease in magnitude depended on environmental conditions at image acquisition and time since event. For HH and HV polarizations, RCR indices indicated statistically significant changes (i. e. , no overlap between CIs) compared to pre-event conditions, except for the dataset acquired under wet conditions (2007. 05. 18). For VV polarization, significant backscatter changes over wind-affected areas appeared only for the last image, acquired four years after the event (2011. 04. 13). Distributions of RCR values were normal over all polarizations and images analyzed. The interval of RCR values (pixel level) was wide and non-outlier ranges largely overlapped (Fig.
2 , lower panels). Such wide intervals are expected when working at high resolution (i. e. , low number of looks) due to the coherent nature of the SAR signal (constructive and destructive interference, which results in speckle). Since the mean values by classes were computed over many pixels, such interferences were filtered out, thereby providing stable mean and CI values. The variable RCR values over stable (unaffected) areas are the result of variations in environmental conditions at the acquisition of post-event datasets, as the same pre-event dataset was used (Fig. 2 ). The amount of change (dynamic range) associated with each RCR, i. e. , the difference in dB between the RCR of affected and unaffected areas, for all post-event datasets varied between 0 dB and 1. 96 dB, depending on environmental conditions and polarization (Fig.
3 ). For all polarizations, the dynamic range observed for the SAR dataset acquired three months after the event was low (0. 1 to 0. 6 dB); the highest values were observed for the dataset acquired four years later. For the image acquired immediately after the event, only the HV polarization showed a dynamic range above the ALOS PALSAR calibration error (0. 5 dB). Areas affected by windthrows were mapped using the HV polarization and the images acquired in April 2007 and 2011. The overall map accuracy was 69% when the dataset acquired in April 2007 was used and increased to 84% when the dataset acquired in 2011 was used; for affected areas, user and producer accuracies were above 54% and 67%, respectively (Table  4 ). Machine-learning classification techniques (results not shown) rendered similar overall accuracies (within 2%), even when multiple independent variables (i.
Insect outbreaks Five pre-event and eight post-event datasets were analyzed to assess sRCR sensitivity to insect-related disturbances. Of the eight post-event datasets, two were acquired under sub-optimal conditions (2009. 07. 01 and 2010. 05) and were only used to evaluate the extent to which such conditions influence the discrimination between affected and unaffected areas (i. e. , not for temporal averaging or thresholds computation). Statistically significant differences were observed for areas marked as outbreaks before the post-event SAR image date when compared to unaffected areas (Fig. 4 ). Stable areas showed sRCR values close to 0 (no change). Increasing effects on forest structural parameters resulted in increasingly negative values of the standardized RCR (postevent values lower than pre-event values).
Analysis of all available datasets indicated that a threshold of -1. 0 dB is a consistent indicator of forest structural changes induced by bark beetle attacks. sRCR values between 0 and -1 indicated lower intensity changes with respect to the reference date, which might be related to the onset of the outbreaks (see values for polygons marked as affected after the analyzed post-event SAR image; Fig. 4 ). Outbreaks recorded in the previous year (e. g. , 2007, for the postevent image acquired in 2008; Fig. 4  upper left panel) have sRCR values around -2 dB, whereas on-going outbreaks (i. e. , 2008) have slightly higher sRCR mean values. One should note that the SAR acquisition date in 2008 was in early summer (June 28), which might explain the smaller changes for the areas affected in 2008 compared to the reference dataset (large parts of affected trees might still be alive, as opposed to the end of summer, when these trees are mostly dead).
Choosing the right pair of images to compute sRCR values when environmental conditions varied over short periods was complex (Fig. 5 ). When individual image-to-image (as opposed to mean-tomean) sRCR ratios were considered, higher dynamic ranges were observed, particularly when the image acquired under wet conditions on July 13, 2007 was used as reference (Fig. 5 , left most panels). Indeed, for the remaining combinations, it seemed considerably more difficult to adequately discriminate affected areas, as most values were close to 0 dB (no change). Such a lower discrimination capacity seems to be related to the drier conditions of the reference datasets  (2007. 08. 28 and 2007. 10. 13 ) as opposed to the wetter conditions for the remaining images. Under dry conditions, some decrease in the forest backscatter is expected due to the decreased vegetation water content.
Management interventions affected RCR values such that areas affected by salvage logging exhibited lower post-event backscatter values The overall classification accuracy varied between 64% and 91%, depending on the outbreak detection date and the sRCR index (Table  5 ). Note that the accuracy was assessed using the reference polygons identified as affected in the previous, current, and past year with respect to the acquisition date of the post-event SAR data used. The lowest overall accuracies (64-74%) were observed for areas affected by outbreaks in the year following the acquisition of the post-event SAR image, which might correspond to the detection of the onset of the outbreak. Areas affected by insect outbreaks in previous and current post-event SAR image acquisition were classified with overall accuracies ranging from 74% to 91% depending on the image pair.
Discussion For both disturbance types, the cross-polarized channel showed the highest sensitivity to changes in forest structure, and a simple mapping approach based on thresholds produced results similar to those of more advanced machine-learning algorithms (results not shown). Classification accuracies were largely in line with those of previous studies  (Ortiz et al. , 2013; Ranson et al. , 2003; Schwarz et al. , 2003). For some datasets, areas susceptible to bark beetle disturbance were identified (at lower accuracies) with about one-year lead time when compared to the reference dataset. By adopting a change-detection framework, distinct advantages were tapped: i) removal of topographic effects, a predominant factor influencing radar scattering, and ii) increased signal-to-noise ratio using multi-temporal averages.
Windthrows Statistically significant changes in backscatter were recorded for areas affected by windthrows. The highest dynamic range between affected and unaffected areas was observed for images acquired under freezing conditions, but one should consider that post-event images acquired under dry conditions were not available. Rainfall negatively influenced the capacity of SAR data to detect changes between acquisition dates. As for other disturbance types, such as logging and fire  (Joshi et al. , 2015; Mermoz and Le Toan, 2016; Tanase et al. , 2010) , the highest sensitivity to forest structural changes was observed for the cross-polarized channel. High backscatter changes generally corresponded to areas affected by windthrows. The overall mapping accuracy (69-84%) was in line with previous results (88%) based on manual interpretation of air-borne X-band interferometric coherence  (Schwarz et al.
Such differences might be explained by the use of change-detection approaches instead of the single-date postevent analysis in the aforementioned studies. Our results were more in line with those of  Fransson et al. (2002) , who demonstrated the utility of long-wavelength SAR (HH polarized, VHF 20-90 MHz) data for the detection of storm-damaged forests. However, the sensitivity of co-polarized channels to windthrows was observed only for images acquired long after the event (four years), and this sensitivity is most likely related to the decreased water content of the remaining trunks (i. e. , the scattering from dry vegetation is lower). On the short term (3 months past event), the co-polarized channels were insensitive to windthrows, which support findings of earlier studies  (Fransson et al.
, 2010; Schwarz et al. , 2003). The low short-term sensitivity might be explained by the interaction of co-polarized waves with vertical or horizontal elements (i. e. , tree trunks, branches) present after the event. After the windthrow, trees lie on the ground and still have their complete needles, branches, and trunks. Moreover, the trees are uprooted, and the root plates stand vertically up to several meters. The trees are still connected to their intact roots and are therefore provided with water. Over the a The same accuracy is the result of using a unique dataset as reference for areas unaffected by disturbance. following seasons, these trees will die, but their vertical structure remains (root plates, trunks laying in a network of several meters with branches and without needles). Because of snow and decay, the trunks will be pressed to the ground, and the branches will fall gradually; these structures will thus have less influence on the backscatter coefficient.
Such reasoning might explain the increased sensitivity for the image acquired in 2011 (i. e. , after the trees were most likely removed or dried up). The rather small overall dynamic range observed immediately after the event for the HV polarization (0. 9 dB) might be explained by the acquisition conditions of the individual datasets (i. e. , spring). Particularly the second dataset (2007. 05. 18) was acquired after rainfall events (Table  1 ). Such conditions might affect the discrimination power, as also shown for other natural disturbances and for cross-polarized channels. The maximum observed dynamic range (-2 dB) was lower than values recorded after other types of natural disasters (-3 dB for areas affected by fires). The difference might be explained by residual backscatter from unaffected vegetation present within the affected pixels (e.
Several limitations affected our study, particularly the SAR density of observations. The lack of pre-event acquisitions precluded the use of FBD datasets, a mode more frequently used within the ALOS PALSAR basic observation scenario. Therefore, a more detailed analysis of the influence of changing environmental factors was not possible. In addition, the lower spatial resolution of PLR datasets did not allow efficient multi-looking and thus did not allow speckle reduction, despite multi-temporal filtering. The availability of datasets with higher spatial resolutions should allow more accurate mapping by reducing miss-detection along borders  (Santoro et al. , 2012)  and by identifying shadowing effects that might be used for identification of the areas affected by storm  (Eriksson et al. , 2012).
Areas susceptible to insect outbreaks might be detected with oneyear lead time, but also with increased commission errors (55%). Early detection, as compared to optic sensors, might be related to tree stages after infestation (green, red, gray). SAR data are sensitive to vegetation water content, which changes during the green stage. However, trees must reach the red or gray stages to be easily detected in the optical datasets. Early detection might become important for forest management by delineating areas likely to be affected by outbreaks, which would allow closer monitoring and longer lead times for tree clearing. Such early detection could not be confirmed for all post-to pre-event image combinations, which might be related to the generally low dynamic range observed for some post-to pre-event ratios and the use of mean seasonal values for the post-event datasets used to calculate the ratios.