Showing papers in "The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences in 2020"

Flood detection in time series of optical and sar images

Abstract: . These last decades, Earth Observation brought a number of new perspectives from geosciences to human activity monitoring. As more data became available, Artificial Intelligence (AI) techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover. Yet, machine learning on SAR data is still considered challenging due to the lack of available labeled data. To help the community go forward, we introduce a new dataset composed of co-registered optical and SAR images time series for the detection of flood events and new neural network approaches to leverage these two modalities.

Hybrid modeling: fusion of a deep learning approach and a physics-based model for global hydrological modeling

Abstract: . Process-based models of complex environmental systems incorporate expert knowledge which is often incomplete and uncertain. With the growing amount of Earth observation data and advances in machine learning, a new paradigm is promising to synergize the advantages of deep learning in terms of data adaptiveness and performance for poorly understood processes with the advantages of process-based modeling in terms of interpretability and theoretical foundations: hybrid modeling. Here, we present such an end-to-end hybrid modeling approach that learns and predicts spatial-temporal variations of observed and unobserved (latent) hydrological variables globally. The model combines a dynamic neural network and a conceptual water balance model, constrained by the water cycle observational products of evapotranspiration, runoff, snow-water equivalent, and terrestrial water storage variations. We show that the model reproduces observed water cycle variations very well and that the emergent relations of runoff-generating processes are qualitatively consistent with our understanding. The presented model is – to our knowledge – the first of its kind and may contribute new insights about the dynamics of the global hydrological system.

A fusion approach for flood mapping using sentinel-1 and sentinel-2 datasets

Abstract: . The frequency of flood events has increased in recent years most probably due to the climate change. Flood mapping is thus essential for flood modelling, hazard and risk analyses and can be performed by using the data of optical and microwave satellite sensors. Although optical imagery-based flood analysis methods have been often used for the flood assessments before, during and after the event; they have the limitation of cloud coverage. With the increasing temporal availability and spatial resolution of SAR (Synthetic Aperture Radar) satellite sensors, they became popular in data provision for flood detection. On the other hand, their processing may require high level of expertise and visual interpretation of the data is also difficult. In this study, a fusion approach for Sentinel-1 SAR and Sentinel-2 optical data for flood extent mapping was applied for the flood event occurred on August 8th, 2018, in Ordu Province of Turkey. The features obtained from Sentinel-1 and Sentinel-2 processing results were fused in random forest supervised classifier. The results show that Sentinel-2 optical data ease the training sample selection for the flooded areas. In addition, the settlement areas can be extracted from the optical data better. However, the Sentinel-2 data suffer from clouds which prevent from mapping of the full flood extent, which can be carried out with the Sentinel-1 data. Different feature combinations were evaluated and the results were assessed visually. The results are provided in this paper.

Advances and Challenges of UAV SFM MVS Photogrammetry and Remote Sensing: Short Review

Abstract: Interest in Unnamed Aerial Vehicle (UAV)-sourced data and Structure-from-Motion (SfM) and Multi-View-Stereo (MVS) photogrammetry has seen a dramatic expansion over the last decade, revolutionizing the fields of aerial remote sensing and mapping. This literature review provides a summary overview on the recent developments and applications of light-weight UA V s and on the widely-accepted SfM - MVS approach. Firstly, the advantages and limitations of UAV remote sensing systems are discussed, followed by an identification of the different UA V and miniaturised sensor models applied to numerous disciplines, showing the range of systems and sensor types utilised recently. Afterwards, a concise list of advantages and challenges of UAV SfM-MVS is provided and discussed. Overall, the accuracy and quality of the SfM-MVS-derived products (e.g. orthomosaics, digital surface model) depends on the quality of the UAV data set, characteristics of the study area and processing tools used. Continued development and investigation are necessary to better determine the quality, precision and accuracy of UAV SfM-MVS derived outputs.

SCAN-TO-BIM for the MANAGEMENT of HERITAGE BUILDINGS: The CASE STUDY of the CASTLE of MAREDOLCE (PALERMO, ITALY)

Abstract: . Conservation and preservation of heritage buildings require the knowledge and sharing of a great deal of data and information about buildings. Such information comes from the different disciplines involved in the restoration and maintenance processes. The integration and use of all this information in a single working environment is a key factor for the success of historical building conservation and management projects. Heritage (or Historic) Building Information Modelling (HBIM) is nowadays the most appropriate tool to collect and manage all data related to Architectural Heritage. The HBIM process requires an in-depth knowledge of the historical building that can be achieved using a detailed 3D survey and adequate parametric modelling. For this reason, the Scan-to-BIM approach, which involves creating the BIM model from a laser scanner survey, is widely used. The work focuses on the application of the Scan-to-BIM process to a historical/archaeological building in Palermo (Italy), the Castle of Maredolce. The work aims to obtain an HBIM of the building but the paper deals also with the survey issues and the modelling challenges, focusing on the different modelling approach between parametric and not-parametric architectural elements. The most difficult challenge of the modelling step was to obtain parametric objects of the complex geometries of the historical building. The work has allowed achieving the HBIM of the Castle of Maredolce and has highlighted some issues and advantages of the Scan-to-BIM approach.

Using multitemporal hyper- and multispectral UAV imaging for detecting bark beetle infestation on Norway spruce

Abstract: . Various biotic and abiotic stresses are threatening forests. Modern remote sensing technologies provide powerful means for monitoring forest health, and provide a sustainable basis for forest management and protection. The objective of this study was to develop unmanned aerial vehicle (UAV) based spectral remote sensing technologies for tree health assessment, particularly, for detecting the European spruce bark beetle (Ips typographus L.) attacks. Our focus was to study the early detection of bark beetle attack, i.e. the “green attack” phase. This is a difficult remote sensing task as there does not exist distinct symptoms that can be observed by the human eye. A test site in a Norway spruce (Picea abies (L.) Karst.) dominated forest was established in Southern-Finland in summer 2019. It had an emergent bark beetle outbreak and it was also suffering from other stress factors, especially the root and butt rot (Heterobasidion annosum (Fr.) Bref. s. lato). Altogether seven multitemporal hyper- and multispectral UAV remote sensing datasets were captured from the area in August to October 2019. Firstly, we explored deterioration of tree health and development of spectral symptoms using a time series of UAV hyperspectral imagery. Secondly, we trained assessed a machine learning model for classification of spruce health into classes of “bark beetle green attack”, “root-rot”, and “healthy”. Finally, we demonstrated the use of the model in tree health mapping in a test area. Our preliminary results were promising and indicated that the green attack phase could be detected using the accurately calibrated spectral image data.

Research on Construction of Spatio-Temporal Data Visualization Platform for GIS and Bim Fusion

Abstract: . The visualization model of GIS and BIM fusion can provide data bearing platform and main technical support for future urban operation centers, digital twin cities, and smart cities. Based on the analysis of the features and advantages of GIS and BIM Fusion, this paper proposes a construction method of the spatio-temporal data visualization platform for GIS and BIM Fusion. It expounds and analyzes the overall architecture design of platform, multi-dimensional and multi-spatial scales visualization, space analysis for GIS and BIM fusion, and platform applications and so on. The urban virtual simulation spatio-temporal data platform project of Teda New District in Tianjin has verified and demonstrated that the effect of application is good. This provides a feasible solution for the construction of spatio-temporal Data Visualization Platform.

EVALUATION OF UNet AND UNet++ ARCHITECTURES IN HIGH RESOLUTION IMAGE CHANGE DETECTION APPLICATIONS

Abstract: . Change detection applications from satellite imagery can be a very useful tool in monitoring human activities and understanding their interaction with the physical environment. In the past few years most of the recent research approaches to automatic change detection have been based on the application of Deep Learning techniques and especially on variations of Convolutional Neural Network architectures due to their great representational capacity and their state-of-the-art performance in visual tasks such as image classification and semantic segmentation. In this work we train and evaluate two CNN architectures, UNet and UNet++, on a change detection task using Very High-Resolution satellite images collected at two different time epochs. We also examine and analyse the effect of two different loss functions, a combination of the Binary Cross Entropy Loss with the Dice Loss, and the Lovasz Hinge loss, both of which were specifically designed for semantic segmentation applications. Finally, we experiment with the use of data augmentation as well as deep supervision techniques to evaluate and quantify their contribution in the final classification performance of the different network architectures.

Managing urban sprawl using remote sensing and gis

Abstract: . Indian cities, like several other developing cities around the world, are urbanizing at an alarming rate. This unprecedented and uncontrolled urbanization may result in urban sprawl, which is characterized by low-density impervious surfaces, often clumsy, extends along the fringes of metropolitan areas with unbelievable pace, disperse, auto-dependent with environmentally and socially impacting characteristics. The ill-effects of urban sprawl in developing countries scenario is a bit complicated compared to that of developed countries because of uncontrolled population growth and haphazard urbanization. This paper attempts to investigate the capabilities of remote sensing and GIS techniques in understanding the urban sprawl phenomenon in a better way compared to time- consuming conventional methods. An overview of the enormous potential of remote sensing and GIS techniques in mapping and monitoring the Spatio-temporal patterns urban sprawl is dealt with here. The spatial pattern and dynamics of the urban sprawl of Kozhikode Metropolitan Area (KMA, Kerala, India) during the period from 1991 to 2018 using the integrated approach of remote sensing and GIS are attempted here. Index derived Built-up Index (IDBI) which is a thematic index-based index (combination of Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI) and Soil Adjusted Vegetation Index (SAVI)) is used for the rapid and automated extraction of built-up features from the time series satellite imageries. The extracted built-up areas of each year are then used for Shannon’s entropy calculations, which is a method for the quantification of urban sprawl. The results of IDBI and Shannon’s entropy analysis highlight the fact that there occurs an alarming increase in the built-up areal extent from 1991 to 2018. The urban planning authorities can make use of these techniques of built-up area extraction and urban sprawl analysis for effective city planning and sprawl control.

Unsupervised Segmentation of Indoor 3d Point Cloud: Application to Object-Based Classification

Abstract: . Point cloud data of indoor scenes is primarily composed of planar-dominant elements. Automatic shape segmentation is thus valuable to avoid labour intensive labelling. This paper provides a fully unsupervised region growing segmentation approach for efficient clustering of massive 3D point clouds. Our contribution targets a low-level grouping beneficial to object-based classification. We argue that the use of relevant segments for object-based classification has the potential to perform better in terms of recognition accuracy, computing time and lowers the manual labelling time needed. However, fully unsupervised approaches are rare due to a lack of proper generalisation of user-defined parameters. We propose a self-learning heuristic process to define optimal parameters, and we validate our method on a large and richly annotated dataset (S3DIS) yielding 88.1% average F1-score for object-based classification. It permits to automatically segment indoor point clouds with no prior knowledge at commercially viable performance and is the foundation for efficient indoor 3D modelling in cluttered point clouds.

Thermal and optical data fusion supporting built heritage analyses

Abstract: . The recent developments of passive sensors techniques, that have been able to take advantage of the technological innovations related to sensors technical features, sensor calibration, the use of UAV systems (Unmanned Aerial Vehicle), the integration of image matching techniques and SfM (Structure from Motion) algorithms, enable to exploit both thermal and optical data in multi-disciplinary projects. This synergy boost the application of Infrared Thermography (IRT) to new application domains, since the capability to provide thematic information of the analysed objects benefits from the typical advantages of data georeferencing and metric accuracy, being able to compare results investigating different phenomena. This paper presents a research activity in terrestrial and aerial (UAV) applications, aimed at generating photogrammetric products with certified and controlled geometric and thematic accuracy even when the acquisitions of thermal data were not initially designed for the photogrammetric process. The basic principle investigated and pursued is the processing of a photogrammetric block of images, including thermal IR and optical imagery, using the same reference system, which allows the use of co-registration algorithms. Such approach enabled the generation of radiance maps, orthoimagery and 3D models embedding the thermal information of the investigated surfaces, also known as texture mapping; these geospatial dataset are particularly useful in the context of the built Heritage documentation, characterised by complex analyses challenges that a perfect fit for investigations based on interdisciplinary approaches.

Generating, storing, updating and disseminating a countrywide 3D model

Abstract: As in many countries, in The Netherlands governmental organisations are acquiring 3D city models to support their public tasks. However, this is still being done within individual organisation, resulting in differences in 3D city models within one country and sometimes covering the same area: i.e. differences in data structure, height references used, update cycle, data quality, use of the 3D data etc. In addition, often only large governmental organisations can afford investing in 3D city models (and the required knowledge) and not small organisations, like small municipalities. To address this problem, the Dutch Kadaster is collaborating with the 3D Geoinformation research group at TU Delft to generate and disseminate a 3D city model covering the whole of the Netherlands and to do this in a sustainable manner, i.e. with an implementation that ensures periodical updates and that aligns with the 3D city models of other governmental organisations, such as large cities. This article describes the workflow that has been developed and implemented.

Using Remote Sensing Images and Cloud Services on Aws to Improve Land Use and Cover Monitoring

Abstract: The Brazilian National Institute for Space Research (INPE) produces official information about deforestation as well as land use and cover in the country, based on remote sensing images. The current open data policy adopted by many space agencies and governments worldwide provided access to petabytes of remote sensing images. To properly deal with this vast amount of images, novel technologies have been proposed and developed based on cloud computing and big data systems. This paper describes the INPE’s initiatives in using remote sensing images and cloud services of the Amazon Web Services (AWS) infrastructure to improve land use and cover monitoring.

Drone data atmospheric correction concept for multi- andhyperspectral imagery – the droacor model

Abstract: . Remote sensing with unmanned aerial vehicles (UAVs) is a fast and cost-efficient tool for mapping and environmental monitoring. The sensors are operated at low flight altitudes, usually below 500 m above ground, leading to spatial resolutions up to the centimeter range. This type of data causes new challenges in atmospheric compensation and surface reflectance retrieval. Based on these specific boundary conditions, a new drone based atmospheric correction concept (DROACOR) is proposed, which is designed for currently available UAV based sensors. It is suited for multispectral visible/near infrared sensors as well as hyperspectral instruments covering the 400–1000 nm spectral region or the 400–2500 nm spectrum. The goal of the development is a fully automatic processor which dynamically adjusts to the given instrument and the atmospheric conditions. Optionally, irradiance measurements from simultaneously measured cosine receptors or from in-field reference panels can be taken into account to improve the processing quality by adjusting the irradiance parameter or by performing an in-flight vicarious calibration. Examples of DROACOR processing results are presented for a multispectral image data set and a hyperspectral data set, both acquired at variable flight altitudes. The resulting spectra show the applicability of the methods for both sensor types and an accuracy level below 2.5% reflectance units.

Automated Marine Oil Spill Detection Using Deep Learning Instance Segmentation Model

Abstract: . This study developed a novel deep learning oil spill instance segmentation model using Mask-Region-based Convolutional Neural Network (Mask R-CNN) model which is a state-of-the-art computer vision model. A total of 2882 imageries containing oil spill, look-alike, ship, and land area after conducting different pre-processing activities were acquired. These images were subsequently sub-divided into 88% training and 12% for testing, equating to 2530 and 352 images respectively. The model training was conducted using transfer learning on a pre-trained ResNet 101 with COCO data as a backbone in combination with Feature Pyramid Network (FPN) architecture for the extraction of features at 30 epochs with 0.001 learning rate. The model’s performance was evaluated using precision, recall, and F1-measure which shows a higher performance than other existing models with value of 0.964, 0.969 and 0.968 respectively. As a specialized task, the study concluded that the developed deep learning instance segmentation model (Mask R-CNN) performs better than conventional machine learning models and semantic segmentation deep learning models in detection and segmentation of marine oil spill.

Sensor fusion, gis and ai technologies for disaster management

Abstract: . Modern Disaster Management Systems are based on several columns that combine theory and practice, software, and hardware being under technological advance. In all parts, spatial data is key in order to analyze existing structure, assist in risk assessment and update the information after a disaster incident. This paper focus on technological advances in several fields of spatial analysis putting together the advantages, limitations and technological aspects from well-known or even innovative methods, highlighting the huge potential of nowadays technologies for the field of Disaster Risk Management (DRM). A focus then is lying on GIS and Remote Sensing technologies that are showing the potential of high-quality sensors and image products that are getting easier to access and captured with recent technology. Secondly, several relevant sensors being thermal or laser-based are introduced pointing out the application possibilities, their limits, and potential fusion of them. Emphasis is further driven to Machine Learning techniques adopted from Artificial Intelligence that improve algorithms for auto-detection and represent an important step forwards to an integrated system of spatial data use in the Disaster Management Cycle. The combination of Multi-Sensor Systems, new Platform technologies, and Machine Learning indeed creates a very important benefit for the future.

Self-learning ontology for instance segmentation of 3d indoor point cloud

Abstract: . Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. The process is built on an unsupervised segmentation followed by an ontology-based classification reinforced by self-learning. We use both shape-based features that only leverages the raw X, Y, Z attributes as well as relationship and topology between voxel entities to obtain a 3D structural connectivity feature describing the point cloud. These are then used through a planar-based unsupervised segmentation to create relevant clusters constituting the input of the ontology of classification. Guided by semantic descriptions, the object characteristics are modelled in an ontology through OWL2 and SPARQL to permit structural elements classification in an interoperable fashion. The process benefits from a self-learning procedure that improves the object description iteratively in a fully autonomous fashion. Finally, we benchmark the approach against several deep-learning methods on the S3DIS dataset. We highlight full automation, good performances, easy-integration and a precision of 99.99% for planar-dominant classes outperforming state-of-the-art deep learning.

Very high resolution land use and land cover mapping using pleiades-1 stereo imagery and machine learning

Abstract: . Anthropocene is featured with increasing human population and global changes that strongly affect landscapes at an unprecedented pace. As a flagship, the coastal fringe is subject to an accelerated conversion of natural areas into agricultural ones, in turn, into urban ones, generating hazardous soil artificialization. Very high resolution (VHR) technologies such as airborne LiDAR or UAV imageries are good assets to model the topography and classify the land use/land cover (LULC), helping local management. Even if their spatial resolution suits with the management scale, their extent covers a few km2, making large-scale monitoring complex and time-consuming. VHR spaceborne imagery has a great potential to address this spatial challenge given its regional acquisition. This research proposes to evaluate the capabilities of a Pleiades-1 stereo-satellite multispectral imagery (blue, green, red, BGR, and near-infrared, NIR) to both model the surface topography and classify LULC. Horizontal and vertical accuracies of the photogrammetry-driven digital surface model (DSM) attain 0.53 m and 0.65 m, respectively. Nine LULC generic classes are studied using the maximum likelihood (ML) and support vector machine (SVM) algorithms. The classification accuracy of the basic BGR (reaching 84.64 % and 76.13 % with ML and SVM, respectively) is improved by the DSM contribution (5.49 % and 2.91 % for ML and SVM, respectively), and the NIR contribution (6.78 % and 3.89 % for ML and SVM, respectively). The gain of the DSM-NIR combination totals 8.91 % and 8.40 % for ML and SVM, respectively, making the ML-based full combination the best performance (93.55 %).

Combining environmental and landsat analysis ready data for vegetation mapping: a case study in the brazilian savanna biome

Abstract: . The Cerrado biome in Brazil covers approximately 24% of the country. It is one of the richest and most diverse savannas in the world, with 23 vegetation types (physiognomies) consisting mostly of tropical savannas, grasslands, forests and dry forests. It is considered as one of the global hotspots of biodiversity because of the high level of endemism and rapid loss of its original habitat. This work aims to analyze the potential of Landsat Analysis Ready Data (ARD) in combination with different environmental data to classify the vegetation in the Cerrado in two different hierarchical levels. Here we present results of a pixel-based modelling exercise, in which field data were combined with a set of input variables using a Random Forest classification approach. On the first hierarchical level, with the three classes savanna, grasslands and forest, our model results reached f1-scores of 0.86, 0.87 and 0.85 leading to an overall accuracy of 0.86. In the second hierarchical level we differentiated a total of 12 vegetation physiognomies with an overall accuracy of 0.77.

Landslide Susceptibility Mapping with Random Forest Model for Ordu, Turkey

Abstract: . Landslides are among commonly observed natural hazards all over the world and can be quite destructive for infrastructure and in settlement areas. Their occurrences are often related with extreme meteorological events and seismic activities. Preparation of landslide susceptibility maps is important for disaster mitigation efforts and to increase the resilience. The factors effective on landslide susceptibility map production depend mainly on the topography, land use and the geological characteristics of the region. The up-to-date and accurate data needed for extracting the effective parameters can be obtained by using photogrammetric techniques with high spatial resolution. Data driven ensemble methods are being increasingly used for landslide susceptibility map production and accurate results can be obtained. In this study, regional landslide susceptibility map of a landslide-prone area in a part of Ordu Province in northern Turkey is produced using topographic and lithological parameters by employing the random forest method. An actual landslide inventory delineated manually by geologists using the produced orthophotos and the digital terrain model (DTM) is used for training the model. The results show that an accuracy of 83% and precision of 92% can obtained from the data and the random forest method. The approach can be applied for generation of regional susceptibility maps semi-automatically.

Evaluation of Semantic Segmentation Methods for Deforestation Detection in the Amazon

Abstract: . Deforestation is a wide-reaching problem, responsible for serious environmental issues, such as biodiversity loss and global climate change. Containing approximately ten percent of all biomass on the planet and home to one tenth of the known species, the Amazon biome has faced important deforestation pressure in the last decades. Devising efficient deforestation detection methods is, therefore, key to combat illegal deforestation and to aid in the conception of public policies directed to promote sustainable development in the Amazon. In this work, we implement and evaluate a deforestation detection approach which is based on a Fully Convolutional, Deep Learning (DL) model: the DeepLabv3+. We compare the results obtained with the devised approach to those obtained with previously proposed DL-based methods (Early Fusion and Siamese Convolutional Network) using Landsat OLI-8 images acquired at different dates, covering a region of the Amazon forest. In order to evaluate the sensitivity of the methods to the amount of training data, we also evaluate them using varying training sample set sizes. The results show that all tested variants of the proposed method significantly outperform the other DL-based methods in terms of overall accuracy and F1-score. The gains in performance were even more substantial when limited amounts of samples were used in training the evaluated methods.

Analysis of Landslide Reactivation Using Satellite Data: a Case Study of Kotrupi Landslide, Mandi, Himachal Pradesh, India

Abstract: . Landslide is a global natural hazard that occurs frequently in the areas of incompetent weak rocks, undulating topography, steep slopes and incessant rainfall. In the night of 12 August 2017, a massive landslide took place at Kotrupi, Mandi district, Himachal Pradesh, India. The slide was so huge that it eroded more than 300-meter stretch of NH-154 killing over 50 people with more than 40 missing. Local residents report that this area has always been unstable where small landslides had occurred in the past. The landslide scar could be seen on the past satellite images from December 2001 to March 2017 on Google Earth. A huge landslide occurred at this location on 13 August 1977. After two decades on 13 August 1997, the landslide reactivated and some part of the slope failed, which can be seen on satellite images of the year 2001. The landslide reactivated again on 13 August 2007, but not much attention was given to it, as it was a small event and did not affect much. Again, after a decade, in the night of 12 August 2017 this landslide was reactivated. There is the possibility of reoccurrence of slope instability from upper reaches of the crown area of the main slide complex as well as the debris, which have been already accumulated on hill and valley side. Based on the geological, geotechnical and geophysical investigations the site stability can be done but its monitoring from satellite provides the information for its future preventive measures.

Synergistic use of remote sensing, gis and hydrological models for study of august 2018 kerala floods

Abstract: . Remote sensing and hydrological models are one of the foremost tools for rapid and comprehensive study of flood hazards and disasters in any parts of the world. Current study is focused on severe 2018 Kerala flood, and is done using various remote sensing data, geospatial tools and combination of hydrological/hydrodynamic/topographical models. Flood mapping is done with pre and post floods remote sensing datasets. For pre-Flood analysis, Normalized Difference Water Index (NDWI) map was prepared on Google Earth Engine (GEE), using Sentinel-2 images for the period of Feb. 2017 to identify permanent water bodies. For post-Flood analysis, GEE was used to download the pre-processed and thermal noise removed Sentinel-1 SAR image for Aug. 9, 2018, Aug. 14 and Aug. 21, 2018 and flood maps were generated using this data. In addition to SAR data, probable flood inundation areas using topography-based flood inundation tool HAND (Height Above Nearest Drainage tool) was also utilized. Hydrological simulation was carried out for all 12 major river sub-basins of Kerala, where floods are reported. Indian Meteorological Department-Global Precipitation Measurement (IMD-GPM) gridded daily data is used as input meteorological data for hydrological simulations. The hydrological simulations results were verified using published Central Water Commission (CWC) reports and reservoirs data for India-WRIS. The hydrodynamic simulation was also performed for simulating the Idukki dam release data and flood condition in downstream areas. Overall, an integrated study and developed approach can be utilized by state and central water and disaster management agencies to develop flood early warning systems.

Automatic conversion of citygml to ifc

Abstract: . The trend of increased usage of both BIM and 3D GIS and the similarity between the two has led to an increase in the overlap between them. A key application of such overlap is providing geospatial context data for BIM models through importing 3D GIS-data to BIM software to help in different design-related issues. However, this is currently difficult because of the lack of support in BIM software for the formats and data models of 3D Geo-information. This paper deals with this issue by developing and implementing a methodology to convert the common open 3D city model data model into the most common open BIM data format, namely CityGML (Groger et al., 2012) to IFC (buildingsmart, 2019b). For the aim of this study, the two standards are divided into 5 comparable subparts: Semantics, Geometry, Geographical coordinates, Topology, and Encoding. The characteristics of each of these subparts are studied and a conversion method is proposed for each of them from the former standard to the latter. This is done by performing a semantic and geometrical mapping between the two standards, converting the georeferencing from global to local, converting the encoding that the two standards use from XML to STEP, and deciding which topological relations are to be retained. A prototype implementation has been created using Python to combine the above tasks. The work presented in this paper can provide a foundation for future work in converting CityGML to IFC. It provides an insight into the relationship between the two standards and a methodology for the conversion from one to the other, and the process of developing software to perform such conversion. This is done in a way that can be extended for future specific needs.

Drivers Of Burned Area Patterns In Cerrado: The Case Of Matopiba Region

Abstract: The Brazilian savanna (Cerrado) is one of the most important biodiversity hotspots in the world. Being a fire-dependent biome, its structure and vegetation dynamics are shaped by and rely on the natural occurring fire regime. Over the last decades, Cerrado has been increasingly threatened by accelerated land cover changes, namely the uncontrolled and intense use of fire for land expansion. This is particularly seen in Brazil’s new agricultural frontier in northeastern Cerrado: the MATOPIBA region. Changes in MATOPIBA’s fire regime resulting from this rapid expansion are still poorly understood. Here we use satellite-derived datasets to analyze burned area patterns in MATOPIBA over the last 18 years, at the microregions level. We further evaluate the role of climate and land use in spatial and temporal burned area variability and assess their trends in the last two decades. Results show an increased contribution of MATOPIBA to Cerrado’s total burned area over the last few years: Maranhao and Tocantins present the highest values of total burned area with some microregions burning more than twice its area over the study period. Climate is shown to play a relevant role in MATOPIBA’s fire activity, explaining 52% of the interannual variance, whereas land use and burned area were found to have more complex interactions that are highly dependent on the regional context. Lastly, climate and land use drivers are found to have an overall increasing trend over the last two decades, whereas burned area trends show much heterogeneity within MATOPIBA.

Using the synergy of qfield for collecting data on-site and qgis for interactive map creation by alkis® data extraction and implementation in postgresql for urban planning processes

Abstract: . The German law concerning Urban Development Promotion (Stadtebauforderungsgesetz; StBauFG) is an important component of the Building Law (Baugesetzbuch). It enables municipalities to be financially supported by the federal and provincial governments for promoting urban development in downtown areas being in need of renovation, maintaining historical centers, or enhancing the value of socially imbalanced areas. Therefore, the law plays an important role for the economical, ecological, social and cultural status of cities. If an urban renovation area is formally declared the reconstruction measures taking place there lead to an upgrading of the real estate land values. The present work contributes on one side to the initial phase for the declaration of an urban renovation area and on the other side to the final phase comprising the legal accounting procedure. At first, the city planners must document the urban status concerning the structure and quality of buildings, vacancy rate for housing and industry, road condition and numerous other urban quality and structure deficits. To acquire these data, the open-source GIS plugin QField serves as an appropriate and easy to handle tool installed on a tablet for the urban planners to collect the necessary data on-site. The planners can then easily assign defined qualities and states of the objects on a map or edit and comment new objects and attributes. Through automatic updating of these data in the PostgreSQL-database, an interactive map in QGIS will then be automatically created in Python. For the last phase, the legal closure of a declared renovation area, our work supports the analysis and modelling of the relationships in the Authoritative Real Estate Cadastre Information System ALKIS® in order to identify the property owners being charged a so-called balance payment for the upgrading of the standard land values resulting from the applied renovation measures. The work shows the high potential of the ALKIS® data being processed with open-source software like PostgreSQL, QGIS, and QField towards a more effective urban planning.

Marker-less mobile augmented reality application for massive 3d point clouds and semantics

Abstract: . Mobile Augmented Reality (MAR) attracts significant research and development efforts from both the industry and academia, but rarely integrate massive 3D dataset’s interactions. The emergence of dedicated AR devices and powerful Software Development Kit (ARCore for android and ARKit for iOS) improves performance on mobile devices (Smartphones and tablets). This is aided by new sensor integration and advances in computer vision that fuels the development of MAR. In this paper, we propose a direct integration of massive 3D point clouds with semantics in a web-based marker-less mobile Augmented Reality (AR) application for real-time visualization. We specifically investigate challenges linked to point cloud data structure and semantic injection. Our solution consolidates some of the overarching principles of AR, of which pose estimation, registration and 3D tracking. The developed AR system is tested on mobile phones web-browsers providing clear insights on the performance of the system. Promising results highlight a number of frame per second varying between 27 and 60 for a real-time point budget of 4.3 million points. The point cloud tested is composed of 29 million points and shows how our indexation strategy permits the integration of massive point clouds aiming at the point budget. The results also gives research directions concerning the dependence and delay related to the quality of the network connection, and the battery consumption since portable sensors are used all the time.

Monitoring and assessment of ecological change in coastal cities based on rsei

Abstract: . With the speeding up of urbanization process, ecological problems, such as unsustainable land use and environmental pollution,have emerged one after another in cites. Nowadays, green development and ecological priority are the important concepts and trends of the current new urban planning in China. In this study, Pingtan County, a coastal city in Fujian Province, China, was taken as the research area. Based on two Landsat 8 remote sensing images (2016, 2017), and two Sentinel-2A remote sensing images (2016, 2017), we first adopt the modified normalized water body index (MNDWI) to mask the water body. Four indicators, including greenness, humidity, dryness and heat were extracted to synthesize the remote sensing ecological index (RSEI), which were obtained by principal component analysis method. Based on the RSEI values acquired from Landsat 8 and Sentinel-2A images, the ecological environment change trend in Pingtan County was evaluated .The experimental results show that: 1) The RSEI indicators based on Landsat 8 and sentinel data all show a downward trend, but due to due to the influence of image spatial resolution and PCA weighting coefficient, the RSEI index has different degrees of decline. 2) The main reason for the decline in RSEI is the increase in NDSI indicators. Compared with July 2016, the bare ground increased in April 2017. Although the NDVI has increased, the overall trend is still declining. Therefore, it is necessary to ecologically return farmland and improve vegetation coverage in the future development process. 3) In recent years, the ecological quality of new construction land near drinking water sources has declined, so it is necessary to strengthen monitoring of changes in the region.

Ground deformation monitoring at continental scale: the european ground motion service

Abstract: . The Persistent Scatterer Interferometry is a powerful technique for ground motion detection and monitoring over wide areas. In the recent years, PSI has undergone a rapid evolution, largely thanks to the launch of the Copernicus Sentinel-1 constellation, the refinement of algorithms, and the increased computational capabilities. These factors allow for using Sentinel-1 interferometric data to develop ground deformation services for wide-area monitoring. Firstly, we review examples of services for national or regional deformation monitoring. The paper then describes the European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service. The EGMS represents a unique initiative for performing ground deformation monitoring on a European scale.

Uav images and deep-learning algorithms for detecting flavescence doree disease in grapevine orchards

Abstract: . One of the major challenges in precision viticulture in Europe is the detection and mapping of flavescence doree (FD) grapevine disease to monitor and contain its spread. The lack of effective cures and the need for sustainable preventive measures are nowadays crucial issues. Insecticides and the plants uprooting are commonly employed to withhold disease infection, even if these solutions imply serious economic consequences and a strong environmental impact. The development of a rapid strategy to identify the disease is required to cover large portions of the crop and thus to limit damages in a time-effective way. This paper investigates the use of Unmanned Aerial Vehicles (UAVs), a cost-effective approach to early detection of diseased areas. We address this task with an object detection deep network, Faster R-CNN, instead of a traditional pixel-wise classifier. This work tests Faster R-CNN performance on this specific application through a comparative analysis with a pixel-wise classification algorithm (Random Forest). To take advantage of the full image resolution, the experimental analysis is performed using the original UAV imagery acquired in real conditions (instead of the derived orthomosaic). The first result of this paper is the definition of a new dataset for FD disease identification by UAV original imagery at the canopy scale. Moreover, we demonstrate the feasibility of applying Faster-R-CNN as a quasi-real-time alternative solution to semantic segmentation. The trained Faster-R-CNN achieved an average precision of 82% on the test set.