dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA sequencing data and analysis Data used for the RNA-seq analysis was obtained from the NCBI (http://www. ncbi. nlm. nih. gov/geo/browse/) or from the Amborella Genome Database, respectively (http://amborella. huck. psu. edu/). Five different tissues (leaves, roots, flowers, flower buds, and seeds/siliques) as well as total seedling were chosen for the analysis of the expression patterns of the genes of interest (Supplementary Table  1 ). The expression was analyzed for 10 species (Supplementary Table  9 ). Reads from RNA-Seq libraries were mapped onto the candidate gene sequences allowing no mismatches using TOPHAT v 2. 0. 14  22  with standard settings and maximum of multihits set at 1, minimum intron length set at 15 bp, and maximum intron length set as 6,000 bp. Reads were added together for each gene using HTseq-count with the overlap resolution mode set as intersection-non empty and with no strand-specific protocol.
lyrata, 4 in Brassica rapa) and TIK (2 in A. lyrata and 1 in B. rapa) homologues were identified, these were also detected in Glycine max (2 WIP, 1 TIK), Prunus persica (1 TIK), Carica papaya (1 WIP), Musa acuminata (1 WIP), A. trichopoda (1 TIK) and the gymnosperm Picea abies (1 TIK) (Supplementary Table  12 ). To expand the data collected by Blastp, a script was developed to detect proteins with the TM domain and C-terminal motif. All the identified plant KASH domain proteins have been divided into 3 groups: SINEs, WIPs and TIK. 40 Six KASH protein clusters were revealed (Supplementary Fig. 2 ). One includes WIP proteins detected in the monocotyledons and the basal angiosperms (Supplementary Table  12 ), as well as 7 new putative WIP proteins to those detected previously by BLASTp. For SINE proteins, 3 clusters were detected, for SINE1/2, SINE3 and SINE4 adding respectively 2, 6 and 12 SINE proteins to those already identified.
The high number of proteins in the SINE3 and SINE4 cluster found only by the script was due to weak conservation of these proteins. One much smaller cluster includes the TIKlike proteins. Only four putative homologues were added but these were shown subsequently to lack either the TIR domain or the C-terminal TM domain, therefore suggesting that the TIK protein 18 may be unique to A. thaliana. An additional cluster (other) had low sequence similarity and was not included subsequently. The three WIP proteins in A. thaliana show previously described properties  38, 39  of a cytoplasmic domain at the N-terminus, with AtWIP1 and AtWIP2 having 3 coiled-coil domains but AtWIP3 only one. The C-terminal region is well conserved and the coiled-coil domains align, with all proteins detected as homologues having a C-terminal predicted TM domain and KASH motif, except AlyWIP1, which lacks homology at the C-terminal region but is well conserved at the N-terminus.
All SINEs have a typical KASH TM domain and Cterminal amino acid motif. 42 The SINE gene family comprises 4 genes in A. thaliana, with similarity between AtSINE1 and AtSINE2, characterized by an Armadillo repeat domain near the N-terminus; and between AtSINE3 and AtSINE4. The Perl script added only 2 sequences in the SINE1/SINE2 group while it added 14 proteins to the SINE3/SINE4 cluster. In this case the Blastp approach was less efficient than the Perl script because of the absence of well-conserved domains in the N-terminus. After removal of sequences with the lowest similarity or without the conserved domain, SINE1/SINE2 proteins are present in all species except the unicellular algae and club moss, while SINE3/SINE4 were absent (Fig. 5 ). In summary, the SINE1/SINE2 cluster and WIP proteins are detected in basal angiosperms whereas the TIK protein is detected only in A.
Study population and data collection The data were collected between 2006 and 2012 in the Stawy Milickie (Milicz fish-ponds) nature reserve (SW Poland) on a 4 ha study plot located on the "Słoneczny Górny" pond (center of the study plot: 51. 5385°N, 17. 3390°E). Each year the study site was monitored from the time of arrival of the first individuals (late April/early May) until the fledging of the last young (mid-August). Due to long-lasting arrival from wintering grounds and high nest losses, breeding was highly asynchronous in our breeding population  [33, 38]. This enabled us to analyse the effect of season progression and nesting stage on survival patterns. Birds were mist-netted and individually marked (combinations of three coloured and one metal ring) throughout the breeding season.
Statistical analysis We adopted nest survival models available in the program MARK, version 7. 1  [43] , to calculate daily survival rates (DSR) of the studied adult reed warbler population. These models can be used for analyzing survival data (of individuals) collected using ragged intervals among animals and over time  [44]. All individuals used for the analyses were adults, and were sexually mature. The first sightings of individuals in a given season, the last sightings of live animals, days the deaths of individuals were discovered, as well as information whether an individual survived the breeding season, were used to generate an encounter history. Julian dates were used, with day 1 defined as the day the first animal was sighted in a breading season. The length of the breeding season was 108 days.
As a number of individuals returned to the same breeding ground each year, some birds were observed during multiple years. However, for the purposes of this analysis they were considered as independent observations. We only included birds observed throughout the whole breeding season, from their settlement in territories in May-June until July-August, when they stopped breeding and started migration. Various models were tested (   We then investigated the possible influence of the progression of the breeding season on DSR values: models dividing the breeding season into 2, 3, and 4 equal intervals were tested (Table  1 , models 5, 6, 7, respectively). Following that, we added the effect of sex to the different interval models-to evaluate whether survival modelled separately for females and males will better suit our data (Table  1 , models 2, 3, 4).
Acknowledgments We are grateful to  Beata Czyż ,  Alicja Dziachan ,  Łukasz Tomasik  and  Magdalena Soboń  for their help in the field. We thank people participating in seminars at the DBE for their input, as well as  Richard Broughton  and  Vicky Stein  for language corrections and their valuable comments on this manuscript. The paper benefited due to constructive remarks of two anonymous reviewers.
Spatial and semantic descriptions of objects, features and processes are derived from one-, twoand three-dimensional (3D) measurements, and the interpretation of their electromagnetic and acoustic signal attributes using active and passive optical, thermal and microwave instruments and sounding devices. Photogrammetry is the science and technology of extracting reliable three-dimensional geometric and thematic information, often over time, of objects and scenes from image and range data. Resultant data can be used for the development of spatial databases and spatial information systems (SIS) in digital, graphical and image forms. The technology is employed for image-based three-dimensional measurements in mapping, engineering, heritage recording, forensic analysis, robotics, driver assistance systems, medical applications, computer gaming and other fields, where it provides geometric and semantic object information for populating spatial databases and for creating virtual reality scenes with real-life textured models.
It is applied in transportation planning and management, urban and infrastructure planning, land and resource management, smart cities, disaster management, environmental monitoring, public health, security, and in understanding many other natural and anthropogenic processes and phenomena. It should be noted that the three topics overlap. Firstly, while photogrammetry is longer established, it is today regarded as part of the wider field of remote sensing. Nevertheless, for the sake of continuity, we will discuss both subjects separately in this paper with an emphasis on terrestrial and airborne images when referring to photogrammetry, and on satellite data when referring to remote sensing. Moreover, photogrammetry forms one of the foundations of modern computer vision  (Förstner 2009). Secondly, while spatial information science is sometimes understood to include data capture, and thus photogrammetry and remote sensing, we take the view here that spatial information science is mainly concerned with making use of the information acquired from images and stored in a database, in order to emphasize our focus on imagery and image exploitation.
2005). Today, the field is also linked to diverse applications such as automatic real-time visual perception, visual navigation, autonomous driving and ambient assisted living which use visual clues to tackle societal challenges such as mobility, health and ageing; it is an extremely active area of research and development. Crowdsourcing, also known as volunteered geographic information (VGI), has added a new dimension to data acquisition. The Internet, and in particular the Geospatial (or Spatial) Data Infrastructure (GDI or SDI), has become key to nearly all aspects of geospatial data, from acquisition to processing, management and analysis in federated databases and data sharing via standardised web services. Digital globes such as NASA World Wind, Google Earth and Microsoft Bing Maps 3D are prominent evidence of these developments.
A number of national, regional and international programs and initiatives have been launched to optimize the exploitation of imagery. Such initiatives aim to provide the right information, in the right place, at the right time, to the right people to make appropriate decisions  (Pereira et al. 2013; Whitcraft et al. 2015). High-quality and high-resolution information is needed for various applications such as coastal monitoring  (Addo et al. 2008 ) and glacier studies  (Kunz et al. 2012). This data is often used not only for monitoring but also for calibrating and validating environmental models. Spatial resolution in earth observation from space has been continuously improved, i. e. , from 80 m for Landsat 1 in 1970s, to 30 m for Landsat 7 in 1990s, and to the current 0. 31 m for WorldView 3 in 2015.
As a result, higher-resolution geospatial data products have been developed at national and even global scale. One recent example is Globaland30 (global land cover dataset of 30 m resolution,  Chen et al. 2014a) , donated by China to the United Nations in 2014. Similar goals are being pursued with the European INSPIRE initiative which strives to harmonise highresolution topographic data across the continent. Further, land cover information at 1 to 2 m resolution has been requested for urban studies  (Belgiu et al. 2014). Such a shift to fine resolution represents a major challenge scientifically, technically and organisationally, which has stimulated intensive research in finer resolution land cover mapping and monitoring  (Townshend et al. 2012; Ban et al. 2015; Chen et al. 2015). The Earth is a dynamic planet with changes taking place at different time scales.
With the advent of multi-sensor systems, including digital frame and video cameras, laser scanners and positioning/navigation devices, high-frequency image sequences are now captured from UAS and mobile mapping vans at normal traffic speed. While automatically interpreting images is a formidable task in its own right (see also Section 3), deriving meaningful and high-quality information from these large datasets presents additional specific research problems which are being tackled using principles from, for example, pattern recognition and machine learning.
Advanced geospatial computing With the rapid advances in remote sensing and crowdsourcing, as well as ground-based sensor networks and computational simulation, highly heterogeneous data of very different origins are being produced, accessed, analysed, integrated, stored and retrieved daily  (Conti et al. 2012). This implies that we are entering a dataintensive world or an era of so-called big data, and are facing another challenge -advanced geospatial computing  (Bryan 2013). Since analysis must often make use of different datasets, geometric and semantic interoperability of these datasets needs to be ensured  (McInerney et al. 2012). Ideally, a web-based spatial information platform able to access federated databases would facilitate the use of these diverse and distributed datasets. In this regard, research and development are needed to solve such key issues as automated crossontology translation and interoperable web services.
2012; Zhao et al. 2012). Many current visualisation systems are domain-or application-specific and require a certain commitment to understand and to learn how to use them, which sometimes hampers their use. New animation and interaction tools such as those being developed in visual analytics  (Andrienko et al. 2014 ) are needed to enable better analysis and enhanced understanding of the massive and dynamic datasets  (Hey and Trefethen 2005).
State-of-the-art in information from imagery In the last few years, we have witnessed significant scientific and technological progress in extracting information from imagery. Key topics include the development of digital aerial cameras, unmanned aircraft systems (UAS) as new flexible platforms, automatic orientation and dense matching of multi-view images, image sequence analysis, automated processing of airborne, terrestrial and mobile laser scanning (lidar) data, very high-resolution optical and radar space sensors, small satellites and satellite constellations, as well as geosensor networks. Other relevant developments include crowdsourcing, linked and big data, federated spatial databases, visual analytics, distributed web-based information services, spatial data infrastructures, as well as open spatial science.
In addition, higher imaging rates allow for increased image overlap, thus resulting in better feature extraction performance, such as point cloud generation from multi-stereo imagery. High-speed mono and stereo cameras are increasingly being used in the automotive industry to record dynamic scenes, e. g. , for car safety investigations; they are also in use in sports. Following the introduction of active sensors some decades ago, we now find that active and passive imaging sensors have more or less the same market share of airborne imaging, characterized by large format digital aerial cameras and lidar systems, while airborne interferometric SAR (InSAR/IfSAR) represents a niche market. On satellite platforms, passive multispectral sensing is the dominant player, though SAR systems have shown remarkable developments and provide a rapidly increasing volume of global geospatial data  (Krieger et al.
Thematic information extraction Thematic information extraction has a long history in photogrammetry and remote sensing  (Mayer 2008; Vosselman 2009 ). On the one hand, detection and reconstruction schemes for individual objects, mostly topographic, from high-resolution images and 3D point clouds have been developed, e. g. , for buildings  (Rottensteiner et al. 2014 ) and roads  (Gerke and Heipke 2008). Image and point clouds have often been processed separately (partly because only one dataset was available), but approaches for combined processing strategies exist. On the other hand, classification methodologies using, e. g. , maximum likelihood classification for satellite images, dating back to the 1970s, have been employed for this task. While classification does not deliver objects per se, the results typically serve as the basis for the detection and delineation of individual objects.
2010; Cao et al. 2014 ) and perform human mobility analysis  (Huang et al. , 2015). In this context, images (e. g. , from Flickr or Instagram) are often used in terms of their metadata (namely, the coordinates and the annotation text) and not necessarily with respect to the image content, which would require image interpretation. At the national level, rapid updating of geospatial databases has become a top priority of national mapping agencies  (Heipke et al. 2008; Chen et al. 2014b). A recent survey conducted by ISPRS with the help of the United Nations Global Geospatial Information Management group (UN-GGIM) shows that some UN member states update their geospatial databases once per year and many other countries have succeeded in completing or updating their mapping requirements at the critical scales 1:5000 to 1:50 000  (Konecny 2013).
Spatial data modelling and analysis Modelling geographical reality and spatial phenomena of interest in digital form is the basis for the construction of geospatial databases, the development of digital earth, virtual globes and smart cities  (Wise 2000; Craglia et al. 2012 ). Spatial analysis is the further interpretation of the digital model to derive meaningful knowledge or to explain the geographic phenomena linked to locations on the Earth surface  (Miller 2004 ). The characteristics of geographic space have significant impact on the content and performance of spatial data modelling and analysis. During the past few years, 3D modelling has become common practice for many larger cities in the world. Many algorithms and data structures were developed for improving the efficiency and quality of city modelling.
Transfer learning can help to adapt a classifier trained on an image set to another dataset having different underlying distributions, or it can help to solve new tasks based on the existing solution of a similar task. Both types of transfer learning should find their way into photogrammetry and remote sensing. Another possibility to cope with too few training data, in particular for mapping applications, is the use of potentially outdated database information. The challenge is to deal with partly wrong information. This issue is referred to the "label noise" problem  (Frénay and Verleysen 2014). For many applications, it is also important that the algorithms "come to know where they fail" in order, on the one hand, to pilot an operator to control and potentially edit and correct the failure, or on the other hand, to swap to an alternative algorithm  (Paparoditis et al.
This would, for example, avoid the need for the generation of orthophotos off-line and lead to the generation of on-the-fly orthophotos. Some open research issues are the consideration and management of uncertainty of both the image orientations and the 3D model in the rendering process and radiometric equalisation of the images (physical, empirical, or halfway between the two) before the blending of textures. Benchmarking: Last, but not least, the ISPRS community should increase their efforts to provide benchmarks for specific tasks  (Rottensteiner et al. 2014; Vallet et al. 2014; Nex et al. 2015). Whereas benchmark datasets from the computer vision and robotics community may be sufficient in many cases, there are some applications that they do not cover, in particular those requiring near-vertical views.
Open geospatial science: Advanced geospatial algorithms: More and more spatial datasets are available which have to be analysed and combined in an intelligent way. To do so, interpretation methods are needed, which are able to find higher level information in the given structured geodata. To this end, similar techniques can be applied as in image analysis, (spatial) data mining and machine learning methods being very powerful techniques  (Miller and Han 2009). As the data is acquired by several sensors, it is obvious that it can also be processed separately, i. e. , in a decentralized way. This has several benefits, e. g. , parallel processing, local computing with advantages concerning data privacy, as well as a reduction in data communication (Duckham 2012). Finally, methods and models are needed which are able to incrementally update and refine geospatial datasets and explicitly allow the descriptions to have different states: e.
Global mapping and monitoring: With open access to global datasets, more efforts will be devoted to their validation, updating and application  (Li et al. 2011; Giri et al. 2013; Chen et al. 2015; Ban et al. 2015). It is necessary to develop internationally agreed technical specifications defining overall strategy, sampling and approaches to quality assessment, and to establish a corresponding web-based validation platform. Operational updating approaches for the generation of more current products and time-series of datasets through a combination of robust change detection, citizen crowdsourcing and ancillary data collation remain to be developed. A number of dynamic monitoring networks to operate at local, regional, national and continental scales will need to be set up for timely delivery of land cover change and other facts, relevant essential variables and trends for decisions makers and relevant users.
Dynamic geospatial services: Recent advances in cyber-infrastructure network and communications technologies, especially the more recent cloud computing technologies, have created both great potential for web-based services and demand for new types of services for disseminating spatial information and accessing massively scaled computing infrastructures, and for new web and cloud services for on-line processing of static and dynamic geospatial and spatio-temporal information and data-intensive problems. The fast transition from Web 2. 0 to Web 3. 0 shifts the web from "content creation" and "participation" to "means and connected knowledge", requiring a semantic web rather than just one that is interactive. Web 3. 0 calls for an "intelligent web" with linked geospatial datasets for more effective discovery, analysis, automation, integration, re-use and visualization of geospatial information across various applications.
Acknowledgements The authors would like to thank  ISPRS First Vice President Orhan Altan ,  Congress Director Lena Halounová , IPAC Chair Gunter Schreier and  Professor Wenzhong Shi  ( The Hong Kong Polytechnic University ),  Professor Stephan Winter  ( University of Melbourne ), for their contributions to the paper. We also acknowledge  ISPRS Honorary Members, Fellows, Technical Commission Presidents  and other ISPRS officers for their valuable comments and suggestions. Finally, the authors thank  Christine Jeans  of  Newcastle University  for her assistance in preparing the figures.
Microclimate models In this paper we focus on the simulation of intra tree crown microclimate modeling. Among the published ecophysiological models  (Green, 1993; Sinoquet et al. , 2001) , the RATP model (Radiation Absorption, Transpiration and Photosynthesis) from  Sinoquet et al. (2001)  was chosen because it has been validated on several tree species and on both photosynthesis, transpiration rate and leaf temperature. The RATP code written in Fortran90 and Python is also available through the OpenAlea platform  (Pradal et al. , 2007). It was designed to simulate the spatial distribution of radiation and leaf-gas exchanges within vegetation canopies as a function of canopy structure, microclimate within the canopy and physical and physiological leaf properties. It is based on a turbid medium analogy for radiation transfers in the canopy described as a set of voxels (Figure  1 ).
Developed model: The 3D tree reconstruction model developed in this study is inspired from  Cao et al. (2010)  and has been developed in Matlab. The algorithm aims to reconstruct an element with a 3D curve skeleton. The original version of Cao et al. (  2010 ) is composed of two steps: a geometric contraction of the point cloud with a local Delaunay triangulation, followed by a topological thinning. A drawback of this method is that for reconstructing a whole tree with complex architecture, it is necessary to perform a previous segmentation in order to isolate each complex part. In our case, it means that every extremity of a branch which bears annual vegetative shoots must be isolated. Given that the algorithm proposed by  Cao et al. (2010)  does not fulfill these requirements, several improvements have been carried out.