Nowadays several scientific disciplines utilize Citizen Science (CitSci) as a research approach. Natural hazard research and disaster management also benefit from CitSci since people can provide geodata and the relevant attributes using their mobile devices easily and rapidly during or after an event. An earthquake, depending on its intensity, is among the highly destructive natural hazards. Coordination efforts after a severe earthquake event are vital to minimize its harmful effects and timely in-situ data are crucial for this purpose. The aim of this study is to perform a CitSci pilot study to demonstrate the usability of data obtained by volunteers (citizens) for creating earthquake iso-intensity maps in a short time. The data were collected after a 5.8 Mw Istanbul earthquake which occurred on 26 September 2019. Through the mobile app “I felt the quake”, citizen observations regarding the earthquake intensity were collected from various locations. The intensity values in the app represent a revised form of the Mercalli intensity scale. The iso-intensity map was generated using a spatial kriging algorithm and compared with the one produced by The Disaster and Emergency Management Presidency (AFAD), Turkey, empirically. The results show that collecting the intensity information via trained users is a plausible method for producing such maps.
Cal/Val activities within the Earthnet Data Assessment Pilot (EDAP) Project of the European Space Agency (ESA) cover several Earth Observation (EO) satellite sensors, including Third-Party Missions (TPMs). As part of the validation studies of very-high-resolution (VHR) sensor data, the geometric and radiometric quality of the images and the mission compliance of the SkySat satellites owned by Planet were evaluated in this study. The SkySat constellation provides optical images with a nominal spatial resolution of 50 cm, and has the capacity for multiple visits of any place on Earth each day. The evaluations performed over several test sites for the purpose of the EDAP Maturity Matrix generation show that the high resolution requirement is fulfilled with high geometric accuracy, although various systematic and random errors could be observed. The 2D and 3D information extracted from SkySat data conform to the quality expectations for the given resolution, although improvements to the vendor-provided rational polynomial coefficients (RPCs) are essential. The results show that the SkySat constellation is compliant with the specifications and the accuracy results are within the ranges claimed by the vendor. The signal-to-noise ratio assessments revealed that the quality is high, but variations occur between the different sensors.
Abstract. The requirement for very high-resolution satellite imagery by different applications has been increasing continuously. Several commercial and government-supported missions provide sub-meter spatial resolutions from optical sensors aboard Earth Observation (EO) satellites. The MAXAR satellite constellation acquires images with up to 30 cm Ground Sampling Distances (GSDs); and the High-Definition (HD) image production technology developed by MAXAR doubles the resolution by using artificial intelligence methods. Although the spatial resolution is one of the most important image quality metrics, several other factors indicated by diverse radiometric and geometric characteristics may circumscribe the usability of data in different projects. As part of mandatory activities of European Space Agency (ESA), Earthnet Programme provides a framework for integrating Third-Party Missions into the overall EO strategy and promotes the international use of the data. The Earthnet Data Assessment Pilot (EDAP) project aims at assessing the quality and the suitability of TPMs, and provides a communication platform between mission providers to ensure the coherence of the systems. In this study, the radiometric quality of the MAXAR HD products was evaluated within the EDAP project framework by using several General Image-Quality Equation (GIQE) metrics, visual inspections, and comparative assessments with orthophotos obtained from an Unmanned Aerial Vehicle (UAV) platform and with the original (non-HD) orthophotos with 30 cm resolutions. The results show that the spatial resolution improvements are observable in urban areas, where sharp edges are present. However, blurring and color noise patterns also occured in the HD images.
Coğrafi Bilgi Sistemlerinin (CBS) teknolojik gelişmeler doğrultusunda kullanımı giderek artmaktadır. Gelişen teknolojik sistemler CBS'nin, sadece masaüstü bilgisayarlar üzerinden değil, aynı zamanda web ve mobil platformlar üzerinde de etkin olarak kullanıldığını göstermektedir. CBS ile kullanıcıların entegre olmasını sağlayan mobil uygulamalar, kullanıcıların sisteme hızlı erişmesini sağlayarak CBS'ye dinamizm katmaktadır. Bu dinamik yapıyı sağlayacak şekilde oluşturulan CBS'ler, kullanıcılardan gelen verileri depolayarak değerlendirebilmektedir. Böylelikle CBS, altlık verinin yanında uygulamanın kullanımına bağlı olarak devamlı bir şekilde güncellenen kullanıcı verilerine de sahip olmaktadır. Kullanıcılar ile CBS arasında bu veri iletişimini sağlamak için internet ortamından faydalanılmaktadır. Bu sistemlerin oluşturulabilmesi için gerekli olan teknolojik altyapıları lisanslama açısından ticari ve açık kaynaklı olmak üzere ikiye ayırmak mümkündür. Açık kaynaklı yazılımlar ile geliştirilen bu çalışmada kullanıcılar tarafından gelen verilerin de depolandığı bir web tabanlı CBS oluşturulmuştur. Sistemin mobil uygulama kısmında Android işletim sistemi, veri tabanı olarak PostgreSQL, harita sunucusu olarak ise MapServer yazılımı kullanılmıştır. Geliştirilen "Sarsıntıyı Hissettim" uygulaması ile kullanıcılar tarafından sarsıntı şiddeti verileri depolanarak bu verilerin harita sunucusu üzerinden görüntülenmesi sağlanmıştır. Ayrıca bu sistem ile bir sivil bilim uygulaması geliştirilerek, sarsıntı hissinin derecelendirilmesi amaçlanmıştır.
Abstract. Determination of discontinuities in rock mass requires scan-line surveys performed in in-situ that can reach up to dangerous and challenging dimensions. With the development of novel technological equipments and algorithms, the studies related to rock mass discontinuity determination remain up-to-date. Depending on the development of the Structure from Motion (SfM) method in the field of close-range photogrammetry, low-cost cameras can be used to produce 3D models of rock masses. However, the determination of rock mass discontinuity parameters must still be carried out manually on these models. Within the scope of this study, a Convolutional Neural Network (CNN) architecture is proposed to identify the discontinuities automatically as the first step for fully automated processing. The Kızılcahamam/Güvem Basalt Columns Geosite near Ankara, Turkey was determined as the study area. The orthophoto of this study area was produced using close-range photogrammetric methods and the training data was produced by manual mensuration. The dataset consists of labeled binary masks and images containing corresponding Red-Green-Blue (RGB) bands. Furthermore, the amount of data was increased by applying augmentation methods to the dataset. The U-Net architecture was used to detect rock discontinuities based on the produced orthophoto. The preliminary results presented here reveal that the discontinuity determination capability of the proposed method is high based on the visual assessments, while problems exist with image quality and discontinuity identification. In addition, considering the small size of the training dataset, the accuracy of the model would increase when a larger dataset could be employed.
Abstract. Rock mass discontinuity and orientation are among the important rock mass features. They are conventionally determined with scan-line surveys by engineering geologists in field, which can be difficult or impossible depending on site accessibility. Photogrammetry and computer vision techniques can aid to automatically perform these measurements, although variations in size, shape and appearance of rock masses make the task challenging. Here we propose an automated approach for the detection of rock mass discontinuities using deep learning and photogrammetric image processing methods. Two deep convolutional neural network (DCNNs) were implemented for this purpose and applied to basalts in Kizilcahamam Guvem Geosite near Ankara, Türkiye. Red-green-blue (RGB) band images of the site were taken from an off-the-shelf camera with 1.7 mm resolution and a 3D digital surface model and orthophotos were produced by using photogrammetric software. The discontinuities were delineated manually on the orthophoto and converted to masks. The first DCNN model was based on the open-source crack dataset consisting of a total of 11,298 road and pavement images, which were used to train the Resnet-18 model (Model-1). The second model (Model-2) was based on fine-tuning of Model-1 using the study data from Kizilcahamam. After fine-tuning, Model-2 was able to achieve high performance with a Jaccard Score of 88% on the test data. The results show high potential of the methodology for transfer learning with fine-tuning of a small amount of data that can be applied to other sites and rock mass types as well.
Abstract. Earthquake, depending on its intensity and location of epicentre, is one of the destructive hazards. Disaster mitigation after a severe earthquake are important to minimize its detrimental effects. Nowadays, several scientific disciplines, such as biodiversity, ecology, geosciences, natural hazards etc., utilize the Citizen Science (CitSci) approaches for various purposes, since the relevant attributes can easily be provided by non-experts with mobile devices. With the CitSci method, disaster related information can be identified and provided rapidly by locals during or after a disaster. Timely, in-situ data after an earthquake can also be collected with CitSci approaches via mobile devices, which can be even more important for all countries. In addition, scientific studies on earthquakes can be enriched and accelerated by using the information provided by volunteers. By collecting reliable data with the CitSci method, the disaster mitigation efforts can be improved, and losses may be decreased. This study aims at developing a CitSci pilot project by using the data collected by volunteers (citizens) to reduce the need for field work in creating earthquake iso-intensity maps and produce them promptly. The present study was based on the 6.8 Mw Elazig earthquake occurred at 20:55 UTC on January 24th, 2020. Through the mobile application “I felt the quake”, the observations of citizens regarding the earthquake were collected. The intensities were revised from the Modified Mercalli Intensity Scale. With the help of data, an iso-intensity map was created and compared to the map produced by The Disaster and Emergency Management Presidency (AFAD), Turkey.
Abstract. The requirement of automated Land Use/Land Cover (LULC) classification has arisen in ecosystem related applications, such as natural hazard assessments, urban and rural area planning, natural resource management, etc. The data source and the classification method used in the production of LULC maps depend on the study area size and the location, and also determined by taking the time and cost into account. Recently, MAXAR Technologies announced a new product, High Definition (HD) with 15 cm resolution, which is obtained by post-processing of images with 30 cm Ground Sampling Distance (GSD). The post-processing employs machine learning methods. On the other side, the effect of HD processing on the image quality, and the usability of such products in various applications are still needed to be investigated. In this study, the influence of HD processing algorithm on LULC classification results was investigated by using 15 cm HD and 30 cm resolution images provided by MAXAR. By using the Random Forest (RF) and Support Vector Machine (SVM) methods in two different study areas, image classification was performed to detect water, vegetation, asphalt road, building, shadow, agriculture and barren land classes. The results show that in HD products, the edges of objects were sharper, whereas the classification noise was higher inside agricultural fields. Considering the overall results, it can be concluded that with the use of HD products in urban areas, improved LULC maps can be obtained.
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