dataesr/scientific-paragraphs-categorization huggingface dataset:
Change detection from traditional optical images has limited capability to model the changes in the height or shape of objects. Change detection using 3D point cloud aerial LiDAR survey data can fill this gap by providing critical depth information. While most existing machine learning based 3D point cloud change detection methods are supervised, they severely depend on the availability of annotated training data, which is in practice a critical point. To circumnavigate this dependence, we propose an unsupervised 3D point cloud change detection method mainly based on self-supervised learning using deep clustering and contrastive learning. The proposed method also relies on an adaptation of deep change vector analysis to 3D point cloud via nearest point comparison. Experiments conducted on a publicly available real dataset show that the proposed method obtains higher performance in comparison to the traditional unsupervised methods, with a gain of about 9% in mean accuracy (to reach more than 85%).
, 2020a] , they are not visually salient and analyzing them requires advanced domain expertise. Thus, environment understanding from optical and SAR data has a significant shortfall in tasks where depth information is critical. An interesting alternative consists in using 3D Point Clouds (PCs) since such data fill the above-mentioned gap and have become more accessible recently. In particular, Light Detection and Ranging (LiDAR) sensor through aerial LiDAR surveying (ALS) allows to obtain 3D PCs at territory scale. While 3D point clouds change detection is popular  [Qin et al. , 2016 , Shirowzhan et al. , 2019 , de Gélis et al. , 2021] , deep learning remains rarely used in this context, calling for more development. Indeed it has been demonstrated that deep learning outperforms both machine learning and traditional methods in numerous remote sensing applications, and compared to rule-based methods, it is less specific to one particular dataset.
To the best of our knowledge, this is the first one that relies on neural networks; 2. A training strategy for deep feature extractor based on self-supervised learning paradigm; 3. An adaptation of DCVA to raw 3D point clouds for bi-temporal deep features comparison; 4. Experimental comparisons to the state-of-the-art, i. e. existing supervised and unsupervised approaches, as well as an application of DCVA on deep features extracted by a pre-trained network on a public dataset for semantic segmentation (transfer learning scenario, as done in  [Saha et al. , 2019]  in the 2D case). This paper is organized as follows. We review related works in Section 2. The proposed method is detailed in Section 3 while experimental validation is presented in Section 4. Finally, a conclusion is proposed in Section 5.
Therefore, to the best of our knowledge, we counted only three studies relied on deep networks to highlight changes into bi-temporal 3D data. Among them,  Zhang et al. [2019]  propose to apply feed forward and Siamese convolutional neural networks on 2. 5D rasterization of PCs into Digital Surface Models (DSMs). The results consists in a binary change label (change or no change) for each 2D patch. In  Ku et al. [2021] , the authors experiment a Siamese architecture based on graph convolution to deal with raw 3D PCs. However, results still remain at the scene/patch level and not at the point level. Finally, de  Gélis et al. [2023]  propose the Siamese KPConv network to perform multi-change segmentation at the point level, in a supervised way. These three fully-supervised methods imply the availability of a fully annotated dataset, which can be challenging in some application contexts.
Indeed, no generic dataset/benchmark exists so far to design change detection methods between PCs at the point level. Therefore, unsupervised approaches constitute an interesting alternative. However, to the best of our knowledge, there are no unsupervised deep learning-based methods to tackle change detection between PCs. Conversely to 3D PCs, change detection in 2D remote sensing images has been far more studied using deep learning based methods since the processing of 2D images is simplified by the regularity of the pixel grid which makes convolutions easier. To tackle the aforementioned difficulties related to data annotation, numerous deep frameworks have been designed to address this unsupervised change detection task. As highlighted in  Shi et al. [2020] , a first category of methods is based on the generation of credible change pseudo-labels to train a deep model.
Only certain pixels are taken into account for the loss computation. Even if it has been shown that final change maps predicted by the deep network are more accurate than the original pseudo-change classification, these methods may somehow be limited by the pseudo-label quality. Therefore, a second category of methods is based on latent change map generated by deep features. Transfer learning is a common strategy to train the deep model to extract useful features  [Saha et al. , 2019]. However, transfer learning still requires the availability of an annotated source dataset. Therefore, fully unsupervised networks such as AEs  [Lv et al. , 2018 , Bergamasco et al. , 2019 , Kalinicheva et al. , 2019 , Touati et al. , 2020 , Zheng et al. , 2021]  or GANs  [Niu et al. , 2018]  are used as well.
Dataset We performed the same experiments on a real dataset consisting of two different ALS campaigns throughout the Netherlands. We extracted some tiles for the training and the test of the method among the publicly available dataset Actueel Hoogtebestand Nederland (AHN). This dataset consists of a total of four surveys throughout the entire country  [Sande et al. , 2010] , allowing multi-date change extraction  [Cserép and Lindenbergh, 2023]. The two last surveys (AHN3 and AHN4) have been semi-automatically annotated to assign a semantic label to each point. Five different classes are distinguished: ground, buildings, water, civil engineering structures (e. g. , bridges) and clutter. This monodate segmentation allowed us to manually derive a change ground truth for the test set. Concerning the changes, we decided to include in change areas the new or demolished building (in this case the building footprint is marked on the ground points), new vegetation and new clutter.
Since our focus is on 3D object changes, our manual annotation was ignoring changes in land cover if not characterized by modifications of the geometry. As such, changes like grassland to bare soil are only discernible using red-green-blue (RGB) color information, and do not imply 3D geometric changes. Besides, let us notice that the RGB data provided in AHN has not been acquired at the same date as LiDAR PCs implying numerous disagreement between 3D geometry and RGB information of changed scenes. Therefore, we explicitly discard RGB information. As far as training PCs are concerned, they are sub-parts of tiles 31HN1_22, 31HN1_23, 31HZ1_04, from the divided AHN dataset (https://geotiles. nl/). Concerning the test set, qualitative analysis is performed on a sub-part of tile 37EN1_08 (see Figure  4(a, c )).
Experimental protocol To assess our proposed self-supervised strategy to extract relevant deep features, we will compare our results with those obtained by our 3D PC DCVA adaptation using a network pre-trained on an annex task such as semantic segmentation using labels from a publicly available dataset as done in the 2D case in  [Saha et al. , 2019]. Indeed, while datasets annotated according to the change are not common when dealing with 3D PCs, public datasets with a mono-date semantic annotation in urban environment are widely spread  [Hackel et al. , 2017 , Roynard et al. , 2018 , Varney et al. , 2020 , Kölle et al. , 2021]. By choosing a public dataset as close as possible to the unlabeled change detection dataset to perform supervised training of the network, one could expect that extracted features will be consistent in unchanged areas and different in the case of changes.
In our study, we use the Hessigheim 3D (H3D) Aerial LiDAR Survey (ALS) to train the network for semantic segmentation. The H3D dataset consists of four different PCs at various dates and comes with labels related to 11 semantic classes that have been manually annotated  [Kölle et al. , 2021]. In practice, the training is performed on H3D PCs acquired in March 2016 on behalf of the national mapping agency of Baden-Württemberg, Germany. In the following study, we refer to this method by Supervised Semantic Segmentation Training (SSST). Combined with DCVA, the method is called SSST-DCVA. Additionaly, even if no specific unsupervised deep method exists so far in the literature for 3D change detection, we decided to compare with the supervised Siamese KPConv network  [de Gélis et al. , 2023]  trained on the simulated Urb3DCD dataset  [de Gélis et al.
, 2021]  and directly applied to our testing set without any retraining. Thereby, this strategy refered as Siamese KPConv transfer is another unsupervised baseline as no label from the target dataset (i. e. , AHN-CD) is used during the training. Conversely, the supervised baseline with Siamese KPConv trained on the AHN-CD train part (with automatic annotation of change) is also given. To further benchmark our method against the state-of-the-art, we provide a comparison with Cloud-to-Cloud (C2C)  [Girardeau-Montaut et al. , 2005]  and multi-scale model-to-model cloud comparison (M3C2)  [Lague et al. , 2013]  distance-based methods. These methods constitute unsupervised baselines for 3D point-based binary change detection  [Shirowzhan et al. , 2019]. In particular, to obtain final binary change information, a thresholding based on Otsu algorithm  [Otsu, 1979]  is applied on the Hausdorff point-to-point distance computed in C2C.
Experimental settings For the same computational reason as 2D images are divided into patches, original PCs are also divided into tiles to run deep learning experiments. As done in the supervised deep framework for 3D PC change detection from de Gélis et al. [2023], vertical 3D cylinders are chosen to make sure that whatever changes occur between the two dates, at least the ground is visible. For the SSST part, KP-FCNN is trained using cylinders of 10 m in radius with a first sub-sampling rate of 0. 2 m. A total of 6,000 cylinders are used for the training at each epoch. The batch size is 10. The 11 H3D classes are fused into 7 classes better suited for the AHN dataset. It requires around 90 epochs to converge. Concerning SSL, the training is realized using cylinders of 20 m in radius and a first sub-sampling rate of 0.
, 2013]. Thereby it might be inadequate for urban environments, as already noticed in de  Gélis et al. [2021]. A distance-based method may not distinguish between topographic and semantic changes as long as the geometry of objects has changed. Learning-based methods (see Figure  3(d-f )) seem to also retrieve small changed objects, which is not possible with distance-based methods without including too many changes. By looking at the results of the Siamese KPConv change detection network with transfer onto AHN dataset from Urb3DCD simulated dataset, we can see that performances are quite similar to the SSST-DCVA but are overtaken by SSL-DCVA method. In particular, some differences with the ground truth are visible in the demolished area and at object boundaries. This is probably due to the difference of building types present in the selected area of AHN dataset.
Indeed, Urb3DCD dataset contains buildings from a french city center different from train and test areas, as for example, AHN3 data (time 1) contains a glasshouse. The same problem occurs with SSST-DCVA methods, since H3D PCs have different resolution and quality than AHN PCs. This shows the advantage of training directly on a dataset with similar properties to the test set and using recent developments in self-supervised learning. However, when compared to the supervised Siamese KPConv network, unsupervised methods can still largely be improved. The main differences of SSL-DCVA with the ground truth are visible on small objects such as vehicles, road signs or vegetation (see Figure  3(c ) and (d)). Furthermore, as can be seen in the buildings on the left side of Figure  3  and right side of Figure  4 (c), some omissions remain on new buildings with a flat roof.
Finally, once again SSL-DCVA seems more interesting than SSST-DCVA when looking at training time. SSST-DCVA takes about 17 hours to train on H3D dataset, while SSL-DCVA only requires about 9 minutes to train (see Table  1 ). The DCVA part on the manually annotated test set takes about 40 seconds. All experiments were realized on a single Graphic Processing Unit (GPU) (Nvidia Tesla V100 SXM2 16 GB).
Conclusion In this paper, we have proposed a method able to detect changes into raw 3D PCs using unsupervised deep learning, i. e. , without any ground truth annotation for the training step. This unsupervised change detection in 3D PCs is challenging due to the lack of point-to-point correspondence between pre-change and post-change 3D points and the proposed method effectively addressed this problem. It further exploits self-supervised learning through deep clustering and contrastive learning to effectively characterize the target area. The method also relies on an adaptation of the deep change vector analysis framework to the particular case of 3D PCs data. Experiments on the public AHN dataset demonstrate both the effectiveness of the proposed approach over existing unsupervised approaches and the additional benefit brought by self-supervised learning w.
r. t. transfer learning (i. e. , using a network pre-trained on another public dataset). More specifically, our method reach 85% of mean accuracy. It futher allows to increase the best traditional and unsupervised learning method by around 9% and 4% of mean of IoU respectively. Nevertheless, the performance of the proposed unsupervised method cannot compete with supervised methods yet, that provide very accurate results, at the cost of tedious annotation of large datasets though. In the future, we will focus on further improving the method in the occluded regions, e. g. , by taking inspiration from the recent literature on contrastive learning in the scene boundaries  [Tang et al. , 2022]. Finally, let us emphasize that our self-supervised method is based on a learning process. As such, it should be agnostic to the sensor used to provide the 3D PCs.
Data collected in the Global COVID-19 Trends and Impact Surveys (UMD Global CTIS), and data on variants sequencing from GISAID, are used to evaluate the impact of the Omicron variant (in South Africa and other countries) on the prevalence of COVID-19 among unvaccinated and vaccinated population, in general and discriminating by the number of doses. In South Africa, we observe that the prevalence of COVID-19 in December (with strong presence of Omicron) among the unvaccinated population is comparable to the prevalence during the previous wave (in August-September), in which Delta was the variant with the largest presence. However, among vaccinated, the prevalence of COVID-19 in December is much higher than in the previous wave. In fact, a significant reduction of the vaccine efficacy is observed from August-September to December.
As another test, we then selected a set with the 20 countries that have the largest number of available responses in the UMD Global CTIS dataset along with South Africa. For each of these countries, the first two Test-positivity rate (TPR) obtained from OWID and extracted from the UMD Global CTIS data for the 20 countries with largest survey data and South Africa. Values of at most 0. 1 are shown in bold. The rest of columns show the Pearson correlation coefficient of each different proxy with the Confirmed time series. Correlation values of at least 0. 9 are shown in bold. The time period used is Jun 18th, 2021 to Dec 31st, 2021. The estimates have been smoothed with a rolling average of 14 days. columns of Table 2 show the official Test Positivity Rates obtained via Our World In Data [RMRG + 20, Our21] (OWID TPR) and the corresponding survey-based estimate from the UMD Global CTIS dataset (CTIS TPR).
Commonly, the ionospheric layer is placed at the height of the maximum electron density. In this study, we use the altitude of 300 km for New Caledonia-New Zealand and 250 km for Chile-Argentina; these are derived from the nearest ionosonde stations situated at NIUE (19. 07°S, 169. 93°W) (Digital Ionogram DataBase, 2022a) and at SANTA MARIA (29. 728°S, 53. 714°W) (Digital Ionogram Data-Base, 2022b), respectively. Furthermore, we use the eruption time calculated by the US Geological Survey (USGS, 2022). To study the water surface response to the HTHH eruption, we use DART observations (NOAA, 2022a) and sea-level measurements (Intergovernmental Oceanographic Commission of UNESCO, 2022). We use 15-s data from 11 DART sites placed near the coastlines of New Caledonia and New Zealand and 15-min measurements from 4 DART buoys along the coastline of Chile (green squares in Figures  1a  and 1b , respectively).
Conclusions The extraordinary HTHH eruption affected the atmosphere and the oceans all over the world. We performed the first joint analysis of the eruption effects in oceanic and ionospheric data in New Caledonia-New Zealand and Chile-Argentina. We recorded the effects of the Lamb wave and of the regular tsunami in both oceanic data and ionospheric observations. The Lamb wave passage did not create remarkable sea-surface fluctuations but was clearly identified in DART data and poorly recognized in tide gauges. In the ionosphere, Lamb wave-related disturbances had a great amplitude (peaking around 1. 1 TECU for both areas). The regular tsunami caused major variations in sea-surface heights (∼1 m near the volcano and ∼2 m along the Chilean coastline), whereas the associated ionospheric perturbation was quite small, half the size of the Lamb wave perturbation.
Acknowledgments This work was supported by the  French Space Agency (CNES), projects "RealDetect" (EA and MR)  and " UV-TECGEOX " (LR and EM), and by the  French National Research Agency (ANR) , projects  ITEC  (Grant  ANR-19-CE04-0003 , LR, EM, and DM) and  IONO-DIET  (Grant.
Data Availability Statement GNSS data are available from the New Caledonia BANIAN network (www. banian. gouv. nc/Map/SensorMap. aspx) from the Chilean CSN (www. csn. uchile. cl/red-sismologica-nacional/red-gps/), and the Argentinian RAMSAC  (Piñón et al. , 2018). DART observations are from the NOAA data archives (www. ngdc. noaa. gov/mgg/ global/ and www. ngdc. noaa. gov/hazard/dart/2022tonga. html). The coastal-sea-level measurements are publicly available via the Intergovernmental Oceanographic Commission of UNESCO (www. ioc-sealevelmonitoring. org/ map. php).
Results We have used the Nançay Decameter Array (NDA), which has been dedicated for more than 4 decades to the daily monitoring of Jupiter's low-frequency (auroral and satellite-induced) radio emissions  [29] [30] [31]. Since mid-2016, a high time-frequency resolution spectrograph called JunoN (for Juno-Nançay) operates at the NDA in support to the Juno mission. It records left-hand (LH) and right-hand (RH) circularly polarized flux densities at resolutions up to 2. 6 ms × 3. 05 kHz in selected time intervals (especially within ± 1-2 days of Juno perijoves)  31. Here we have analyzed a total of 24 hours of highresolution JunoN data recorded over one month (April 2021), representing approximately 67 million spectra over 16384 frequency channels each, and a data volume > 4 Terabytes. The data have been sliced in approximately 160000 dynamic spectrograms covering each 1.
1 s × 9 MHz, in which drifting bursts show up after mitigation of radio frequency interference (RFI). As it is impossible to analyze visually such a large number of time-frequency images, we have developed an automated pipeline that detects the presence of drifting features in a dynamic spectrum and measures their drift rate and signal-to-noise ratio (SNR). The pipeline, which includes several steps (RFI mitigation, 2D FFT followed by Radon transform, and Gaussian fit of the dominant peak in the Radon spectrum) is described in details in the Methods section. With it, we have detected fast drifting fine structures in 24% of the RH dynamic spectra (corresponding to emissions from Jupiter's northern hemisphere) and 18% of the LH ones (southern hemisphere) (Table  1 ). These high percentages result from the pre-selection, by the NDA operator at the end of each JunoN observation, of data intervals potentially containing fine structures.
Acknowledgements This article is based on observations made with the  Nançay Decameter Array  (NDA). The NDA is hosted by the  Nançay Radio Observatory  of the  Observatoire de Paris (ORN , supported by  CNRS ,  Université d'Orléans, OSUC , and  Région Centre in France). We thank  B. Cecconi  and  E. Thétas  for their help in making the data available, and  F. Jacquet  (the  NDA operator ) for his daily data inspection work. P. Zarka  acknowledges funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No  101020459 -Exoradio ).
Data availability The data analyzed are referenced and available at https://doi. org/10. 25935/PBPE-BF82 (Lamy, L. , G. Kenfack, P. Zarka, B. Cecconi, C. Viou, P. Renaud, F. Jacquet, A. Loh, L. Denis, A. Coffre: 2021. Nançay Decameter Array (NDA) Jupiter Juno-Nançay data collection (Version 1. 0), [Data set], PADC (2021). The data obtained as a result of this analysis are available at https://doi. org/10. 5281/zenodo. 8098758,  77. The derived data used to produce the figures in this study are provided in the Source Data file. Source data are provided with this paper.
The extracted binders were also characterized by simulated distillation to assess their volatile fractions. Details of the method and results are available in Section 1. 3 of ESI. † The chromatograms obtained by the simulated distillation process (see Fig. S3  of ESI †) demonstrated the evolution of the mass of evaporated product as a function of retention time in the column. Briey, the light (carbon number < C35) and heavy (carbon number > C35) fractions were not the same in fresh and old bitumens. This observation may account for part of the differences observed between the emissions from the different types of asphalt mixtures. 2. 1. 3. Environmental conditions experienced by road asphalt mixtures. We have measured asphalt surface temperatures on the road and parking lot facing our laboratory with an infrared thermometer under different weather conditions determined by a station located on top of the building hosting our laboratory; the data are presented in Lasne et al.
Aer one hour spent at a xed temperature, VOC concentrations were stable as observed by PTR-ToFMS. A typical signal recorded at M + 1 = 71 Da during a step-by-step annealing of the STA3 asphalt mixture is shown on Fig. 2. The signal was considered constant in the second hour of each temperature step. Hence, emissions were determined from data recorded during the second hour of each temperature step. This raises the question of the exhaustion of the emissions from each sample by the temperature cycles in the experimental chamber. Firstly, note that old asphalt pavements have been exposed to the atmosphere for years and yet VOC emissions were still detectable in the laboratory. Hence, these samples were not exhausted and their emissions were at steady-state. Regarding fresh asphalt mixtures, their emissions were monitored over longer timescales and were found constant within experimental uncertainty.