dataesr/scientific-paragraphs-categorization huggingface dataset:
This work addresses the generation of high quality digital surface models by fusing multiple depths maps calculated with the dense image matching method. The algorithm is adapted to very high resolution multi-view satellite images, and the main contributions of this work are in the multi-view fusion. The algorithm is insensitive to outliers, takes into account the matching quality indicators, handles noncorrelated zones (e. g. occlusions), and is solved with a multi-directional dynamic programming approach. No geometric constraints (e. g. surface planarity) or auxiliary data in form of ground control points are required for its operation. Prior to the fusion procedures, the RPC geolocation parameters of all images are improved in a bundle block adjustment routine. The performance of the algorithm is evaluated on two VHR (Very High Resolution)-satellite image datasets (Pléiades, WorldView-3) revealing its good performance in reconstructing non-textured areas, repetitive patterns, and surface discontinuities.
Paradoxically, from the standpoint of 3D reconstruction precision, the larger the B=H, the better the point intersection angles, hence the more precise coordinates' estimation. The above, the increased availability, and the demand for versatile datasets motivates the research presented in this work. We lay down a 3D reconstruction pipeline focusing on a global 3D fusion algorithm, implemented in the free open-source software tool for photogrammetry -MicMac  (Rupnik et al. , 2017). The contributions are: -unlike most current approaches applied to satellite datasets the approach is not limited to stereo-pairs processing  (Reinartz et al. , 2005; Karkee et al. , 2008; Hirschmüller, 2008; Xu et al. , 2010; Kuschk and dAngelo, 2013)  but works with multi-view configurations of varying base lengths and resolutions; -it is defined in 3D and handles occlusions (Section 3.
2); -it is insensitive to outliers and weighted by the image matching confidence indicator (Section 3. 3. 2); -it is formulated as a discrete, semi-global optimization problem therefore circumvents the artefacts pertinent to local methods while being computationally and memory-use efficient (Section 3. 3. 3); -no geometric constraints (e. g. surface planarity) are assumed and no auxiliary data (e. g. ground control points) are required. The implemented methods are tested on numerous multi-view satellite image configurations using two datasets -a rural zone captured in a single epoch and an urban zone captured at various epochs across the year. All results are evaluated qualitatively and quantitatively by comparing them with ground truths of superior quality (only urban area). The publication is organized as follows: Section 2 provides a concise literature review on state-of-the-art 3D fusion approaches; Section 3 addresses the reconstruction pipeline available in Mic-Mac with the focus on the theoretical aspects of the employed fusion methodology; and finally, the experimental part is included in Section 4.
(2013)  proposes an improved, hybrid version of both the VRIP and the work of  Fuhrmann and Goesele (2011). The authors differentiate between changing 3D point's quality due to image acquisition geometry by adding a stochastic component to the distance function. Hence the fusion is rendered more rigorous. Despite being precise, all three approaches suffer from a large computational burden making them inappropriate for big datasets. Complex 3D objects. Semi-global and global approaches. If no regularization is adopted such averaging techniques effect in noisy reconstructions or surface inconsistencies due to the mean distance field sign changes  (Zach, 2008). Consequently,  Zach et al. (2007)  and  Zach (2008)  adopt a global variational approach. The data term is a signed distance function weighted with depth uncertainty and the regulariser is the first order total variation.
Karkee et al. (2008)  take two DEMs generated within the SRTM and ASTER missions and completes them in void places with an erosion algorithm. Xu et al. (2010)  create a hybrid product being a weighted average of a TerraSAR-X DEM and a DEM calculated from ALOS imagery. In all cases 2. 5D surfaces generated from heterogeneous sensors are exploited to produce a more complete 3D surface. Next,  Papasaika et al. (2011)  conceived a local fusion using a dictionary of implicit functions. Kuschk and dAngelo (2013)  adopted a global energy formulation to fuse depth maps obtained by heterogeneous satellite sensors. The algorithm is a variational method, inspired by  Pock et al. (2011). Using synthetic datasets and a World-view 2 images they showed superiority of their approach to the median-based fusion.
Fusion algorithm At this stage the algorithm has at the disposal several depth maps registered in some absolute RF, with their respective normalized cross-correlation (NCC) maps and 2D occlusion masks. The objective is to combine all this information in an optimal way and obtain a single depth map. The processing workflow is presented in Fig. 4. To start with, the dataset is partitioned into smaller processing blocks that are in the following executed in parallel. The algorithm starts by depth clustering and outlier removal. It then identifies the most probable depth within every cluster and launches the multi-directional dynamic programming. The dynamic programming performs the typical set of operation, i. e. initialization of the cost structure; cost fill-in; cost aggregation; and lowest cost depth selection.
All computations were done in MicMac. Conversion of the LiDAR point cloud to a raster format was done in LASTools  (Isenburg et al. , 2006). Urban area. The urban dataset is a collection of multi-temporal, multiple view stereo panchromatic images captured with the WorldView-3 satellite (GSD = 30 cm), accompanied by a LiDAR ground truth (20 cm raster) (cf. Fig. 6 ). It was made available publicly within the framework of IARPA's Multi-View Stereo 3D Mapping Challenge 2 (Satellite Benchmark JHU/APL, 2016). We refer to it as the MVS benchmark. The RPC parameters of the entire dataset (i. e. including images from all scenarios) were adjusted in a single bundle adjustment routine without the use of ground control points. Four acquisition scenarios were selected for the evaluation, see Fig. 7. In each scenario the multi-view reconstructions are grouped in image triplets, starting with a 4-triplets combination in a single orbit (S1) and ending with 13 triplets from multiple orbits (S4).
Moreover, within all scenarios we distinguish industrial and residential area types (cf. Tables  1, 2 ). The positions of the LiDAR and the photogrammetric DSM rasters are of different resolutions and do not coincide in space. The DSM Pho was hence resampled to the resolution of the DSM LiDAR , whereas the new positions were interpolated with a bilinear inter-Fig. 16. Video dataset, the gray-shaded DSMs. Then intensities were multiplied by a scalar in order to ease the visual interpretation. The green ROI corresponds to an extract presented in Fig. 17. Scenarios (1)-(  4 ). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. ) Fig. 17. Video dataset, an extract from the gray-shaded DSM (compare Fig. 16 ). Then intensities were multiplied by a scalar in order to ease the visual interpretation.
82 m as the horizontal RMSE, 4. 12 as the error rms , 0. 91 as the error median and 0. 53 in the completeness score. Visual inspection reveals a noisier but apparently more detailed surface reconstruction by our algorithms. Rural area. The rural dataset is a Pléiades video acquisition (cf. Fig. 14 ) comprising of 18 images, and we refer to it as the video dataset. The RPC parameters of the 18 images were adjusted in a single bundle adjustment routine with the use of a few ground control points. Again, four acquisition scenarios were chosen (cf. Fig. 15 ) with the objective to identify the optimal results in terms of accuracy, by comparing them with a reference dataset. Since no ground truth was available, out of four scenarios we picked as reference the DSM of best visual quality (scenario3, cf.
Conclusions This publication presents a 3D reconstruction pipeline adapted to modern multi-view satellite acquisitions. The pipeline follows a refinement of the sensor's geolocation (via bundle block adjustment), per n-tuplet image dense matching, the terrain geometry transfer phase and finally the fusion of individual depth maps. The fusion -the essence of this work -is formulated in a semiglobal optimization framework, takes into account the occlusions and other non-correlated zones as well as quality indicators resulting from n-tuplet image dense matching. The algorithm makes no a priori on the resolution of the to-be-fused depth maps hence handles DSMs resulting from different resolution sensors. All the above is implemented in the free open-source software for photogrammetry -MicMac. The pipeline was tested on two datasets acquired with Pléiades and WordView-3 satellites, and evaluated in a quantitative and qualitative manner.
Acknowledgements This research was funded by  CNES  via the TOSCA programme. The Pléiades video acquisition was obtained through the  CNES ' ISIS framework. The satellite imagery in the MVS benchmark dataset was provided by the courtesy of DigitalGlobe. The numerical computations were performed on the SCAPAD cluster processing facility at the  Institute de Physique du Globe de Paris. We would like to kindly thank  Myron Brown  and  Bosch Ruiz  for providing us with the Multi-View Stereo 3D Mapping Challenge contest results.
This methodology allowed us to measure a large number of cell cycle phase-and cell size-associated variables in 22 mutants, totaling up to 15,000 cell cycles per genotype. Using this dataset, we quantitatively established the existence of a compensatory growth mechanism operating on the bud size during G2 in WT cells, thus confirming the existence of multiple sizedependent inputs in size control  (Harvey and Kellogg, 2003; McMillan et al. , 1998) , in agreement with theoretical predictions  (Thomas W Spiesser, 2015) , and clearly ruling out the "cryptic" nature of size control in G2. Unexpectedly, among the cell cycle genes tested that affect size compensation in either G1 or G2, we found that genes related to the regulation of cyclin B-Cdk activity had the strongest impact on size homeostasis.
6 min after the end of anaphase. We could not exclude the possibility that this procedure would introduce artefacts regarding measurements of G1 duration in mutants with cytokinesis defects. Yet, none of the reported mutants reported below, with the exception cdh1  (Tully et al. , 2009) , have been described to affect cell cytokinesis. Similarly, we used the Whi5-mCherry fusion protein to assess the coordination between cell cycle Start (as defined by nuclear exit of the transcriptional repressor Whi5) and the onset of histone synthesis (Fig. To extend this preliminary analysis, we developed custom MATLAB software Autotrack to automate the processes of cell and nucleus contour segmentation, cell tracking, histone content measurement, and mother/daughter parentage determination (Figure  1  -Figure supplement 6 and Supporting Information).
We then used a piecewise linear model to identify the histone synthesis plateaus and ramp in the raw data, which allowed us to extract 4 intervals per cell cycle (Figure  1B-C  and Video 1): G1 (plateau), S (linear ramp), G2/M (plateau preceding anaphase), and the interval between anaphase onset and cytokinesis (referred to as "Ana"), taking into account our hypothesis that the period between the end of anaphase and cytokinesis was constant, as mentioned above. Using this method, we extracted the duration of cell cycle phases for up to ~500 cells in each of the 8 cavities in each independent chamber. By pooling 17 replicate experiments, we collected ~26,900 cell cycles for WT cells (Fig. 1C ) of which 63% passed our quality control procedure aimed at discarding cells with segmentation/tracking or data fitting issues (see Supporting Information and Figure  1   Using this analysis, we found that the cell cycle durations for WT cells were in good agreement with data obtained using other markers or methodologies.
2D ), in agreement with previous measurements  (Pramila et al. , 2006; Surana et al. , 1991). Collectively, these results obtained in various mutant backgrounds further establish proof-of-principle for our methodology, in which a single fluorescent marker enables simultaneous measurements of key events associated with cell cycle progression. In total, we monitored the dynamics of cell cycle progression of 22 mutants. The raw cell cycle data are available on a dedicated server  (Tassy et al, 2018)  that allows detailed data exploration and on-the-fly statistical analyses (see Appendix 1).
Compensatory Mechanism Our ability to measure the duration of specific cell cycle phases provides a unique opportunity to investigate in detail the coordination of growth and division during each phase of the cell cycle. We extracted 15 variables (e. g. , phase durations, bud/cell volumes and growth rates during unbudded and budded period; see Supporting Information) describing cell cycle progression in both mother and daughter cells. Cell volumes were computed from segmented cell contours assuming an ellipsoid model. Using this dataset, we sought to identify novel compensatory effects reflecting the existence of size control mechanisms. For this, we systematically measured the Pearson correlation coefficient for all measured distributions of variables in mothers and daughters (Fig. 3A  and 3B ).
Discussion In this paper, we have enabling correlative measurements to be made using additional spectrally independent markers. The large throughput of the image acquisition/processing pipeline developed in our study provides the opportunity to detect mild yet meaningful phase duration phenotypes that were not detected in previous analyses. The discovery that cells replicate their DNA more rapidly with increasing replicative age is a good example of this ability to resolve small differences in cell cycle timing, although this observation needs to be confirmed using complimentary techniques. Since we could not exhaustively analyze the large datasets generated in this study within the scope of this article, we have created a dedicated server to allow further statistical analyses of cell cycle variables in individual cells.
Dataset Management and Online Data Publishing All variables extracted during image processing were stored in a mutant-specific database designed to allow straightforward analysis using custom MATLAB software, as described in the Supporting Information. We developed a web application, Yeast Cycle Dynamics, that allows custom statistical analysis of extracted cell cycle data for all mutants in this study. In addition, raw data showing histone levels and cell size as a function of time for all cells can be monitored  (Tassy et al, 2018). See Supplemental Information for details. Figure Legends.
Autotrack: software for automated extraction of cell cycle phases Autotrack is software developed during this study to automate the processing of timelapse datasets. It is based on the project architecture of PhyloCell, which is frontend software previously developed by our group to process time-lapse images. Both programs are available for download at github: https://github. com/gcharvin. The software allows parallel segmentation, tracking, and quantification of individual cells in yeast microcolonies. Information on both the cell and nucleus is used to ensure a reliable determination of cellular parentage (mother/bud relationships), which is mandatory for assessing cell growth during a complete cell cycle (see Figure  1  -Figure supplement 6 for an overview of the pipeline). Finally, a specific routine is used to extract the duration of individual phases for each cell cycle, and a quality control routine is run to discard outliers.
g.  2 , tdiv, tg1, etc. ) mentioned above (see Figure  1  -Supplement 7A). For instance, this analysis showed that abnormal histone curve fitting contributes to 15% of the total rejected cell cycles (using the arbitrary threshold  2 =0. 01, see Figure  1  -Figure  supplement 7A  and B ). Then, we have compared the cell cycle timings of selected cell cycles versus the whole set of data (see Figure  1  -Figure  supplement 7C ). This analysis revealed that data filtering mostly changes the duration of G1, by discarding cell cycles with abnormally short G1 in daughter cells. Therefore, this procedure is useful to get rid of major artefacts associated with the automated tracking and fitting procedure. However, we checked that data filtering does not qualitatively change the results related to compensatory growth, even though the values of the corresponding slopes are slightly different in the 'selected' versus 'all' cell cycle datasets (see Figure  1   Different mutants can be compared thanks to our dynamic graph system.
The distance segregating mutants can be computed for all or every combination of variables. This way, it is possible to see which lines behave similarly for a given set of parameters. This tool uses the Mahalanobis distance which minimize the influence of the noisiest datasets for this measure. Also, a principal component analysis module has been developed to better identify the variables that are the most discriminant among the mutants. Variables are represented as vectors which direction and length show their relative influence in the observed behaviors. Last, it is possible to select specific yeasts from the database based on the volume of their body, bud and nucleus as well as the time duration for every phase of the cell cycle. The characteristics of the resulting cells can be further explored using their individual file.
Each page shows the cell identity, its genotype, the number of reported divisions and a summary of all the variables recorded during the procedure. The system also generates an interactive graph showing the evolution of the fluorescence signal as well as the volume of the cell and its bud over time. When yeasts have been tracked during several divisions, the user can display these features for every step to see how they evolve in time. YCD is developed in PHP v. 5 and JavaScript. The graphs are generated using R v. 3 and the Rgraph library. Data are stored and accessed from a PostgreSQL v. 9 database. The gene descriptions used in the system have been downloaded from the Saccharomyces Genome Database. The whole dataset, including single cell data for each mutant, is available upon request.
Acknowledgements We are very grateful to  Damien Coudreuse  for extensive discussion and feedbacks on the manuscript,  Manuel Mendoza  and  Etienne Schwob  for insightful discussions, as well as  Sophie Quintin  and the  Charvin lab  for careful reading of the manuscript. We thank  Sandrine Morlot  for help with microscopy. We thank  Michaël Knop  for sharing plasmids and  Joseph Schacherer  for the gift of the cell cycle mutants. This work was supported by the  ATIP-Avenir program  (G. C. ), a grant from the  Fondation pour la Recherche Médicale  (G. C. ), and by grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02.
Surface soil moisture (SSM) controls the the energy and water transfers between soil, vegetation and atmosphere. A better representation of this variable in Land Surface Models (LSMs) could reduce significantly the uncertainties associated to future climate predictions. Thanks to its large spatio-temporal coverage, the new ESA CCI multiinstruments dataset offers a good opportunity to benchmark LSMs and improve their parametrization with data assimilation techniques. In this study, the ESA CCI surface soil moisture (SSM) combined product is confronted with the different simulated soil layers of the ORCHIDEE LSM (the continental part of the IPSL earth system model), in order to assess whether it can be used to improve the model using data assimilation.
INTRODUCTION Soil moisture plays an important role in the water, carbon and energy exchanges between the land surface and the atmosphere. It is a vital variable which needs to be better understood and controlled since it links these critical cycles. In particular, due to the coupling between water and carbon cycles at the leaf level, it is a major constraint for the assimilation of carbon by the vegetation through photosynthesis. A better representation of this variable in Land Surface Models (LSMs) could significantly reduce the uncertainties associated to future climate predictions. To improve the representation of such variables in LSMs, the model can be confronted with the observations. These observations can be measured in situ or remotely, for example, using satellites. Since soil moisture is difficult to observe at large scales due to its high spatial and temporal variability, satellite data can provide a much needed consistent global dataset.
The new ESA CCI multi-instruments dataset has a large spatio-temporal coverage. This offers a good opportunity to benchmark LSMs and, through data assimilation techniques, to improve their parametrizations. When working with this product however, we must be wary of its multiconstellation nature. Various studies have already evaluated the quality of the ESA CCI SM product at a global scale. Liu et al. (2011 [1] , 2012  [2] ) showed that merging active and passive products increases the number of observations while minimally changing the accuracy. The final blended product was shown to carry the relative dynamics of the original passive and active microwave retrievals. Subsequently,  Dorigo et al. (2012 [3] ) revealed that most of the major trends in the blended product between 1988 and 2010 were also visible in modeled and in situ measured dataset.
Albergel et al. (2013 [4] ) demonstrated that product performed well in capturing the annual cycle of SSM and its short-term variability by comparing it to re-analysis and in situ measurements. Loew et al. (2013 [5] ) undertook the task of summarizing potential and limitations of the ESA CCI SM product for model evaluation. They mentioned its promising use for data assimilation in LSMs. Finally,  Dorigo et al. (2015 [6] , 2017  [7] ) validated previous studies by indicating that the merged product has a similar or better quality than the individual products and that quality shows an upward trend over time by confronting it to a large number of in situ observations. Further studies have already shown the potential of such product to study land-atmosphere-biosphere interactions (  [8] ,  [9] ) and agree that assimilating together active and passive microwave observations give maximum accuracy and coverage to improve LSM (  [10] ).
These results give encouraging prospects on the ability of the ESA CCI SM product to improve land surface modeling with data assimilation techniques. The comparison of observations with the model output is a mandatory first step in data assimilation. This vital step allows us to firstly assess whether the physical properties in both the observations and model output are comparable, and secondly to explore their different weaknesses and errors. It is a method used to evaluate whether the observations have the ability to improve the model through assimilation or not. When comparing the ESA CCI SM satellite data and the output from a LSM, three limitations need to be kept in mind: • SSM variability is largely driven by precipitation and therefore the meteorological forcing data used to run the LSM might have a greater weight on the comparison scores than the parametrisation of the model itself.