The seismic risk mitigation plans are vital since vulnerable structures are prone to partial or total collapse under the effect of future major earthquake events. Therefore, vulnerable structures in large building stocks should be determined using robust and accurate methods to prevent loss of lives and property. In the current state‐of‐the‐art, the risk states (i.e., whether risky or not) of structures completely depend on the experience of the reconnaissance team of engineers, which could not result in standardized decisions. In this study, machine learning has been integrated into the decision‐making algorithm to classify more precise and reliable seismic risk states of masonry buildings, categorizing them into up to four risk categories. For this purpose, a large database, including 12 features and detailed seismic risk analysis results of 4356 masonry buildings, is formed. Firstly, the input variables are preprocessed using feature engineering methods. Then, several machine learning algorithms are utilized to produce a network to estimate the risk state of masonry buildings in association with the risk states obtained from the detailed analysis results. As a result of the analysis of these algorithms, the correct prediction percentages for the testing database of the proposed method for two, three, and four risk states classification are predicted as approximately 87.5%, 86.6%, and 79.0%, respectively. This new approach makes it possible to produce risk color maps of large building stocks and reduce the number of buildings that require immediate action.

This article introduces the Special Issue on “Citizen Science and Geospatial Capacity Building” and briefly evaluates the future trends in this field. This Special Issue was initiated for emphasizing the importance of citizen science (CitSci) and volunteered geographic information (VGI) in various stages of geodata collection, processing, analysis and visualization; and for demonstrating the capabilities and advantages of both approaches. The topic falls well within the main focus areas of ISPRS Commission V on Education and Outreach. The articles collected in the issue have shown the enormously wide application fields of geospatial technologies, and the need of CitSci and VGI support for efficient information extraction and synthesizing. They also pointed out various problems encountered during these processes. The needs and future research directions in this subject can broadly be categorized as; (a) data quality issues especially in the light of big data; (b) ontology studies for geospatial data suited for diverse user backgrounds, data integration, and sharing; (c) development of machine learning and artificial intelligence based online tools for pattern recognition and object identification using existing repositories of CitSci and VGI projects; and (d) open science and open data practices for increasing the efficiency, decreasing the redundancy, and acknowledgement of all stakeholders.

Precise, seamless, and high-rate navigation remains a major challenge, particularly when relying on low-cost sensors. With the decreasing cost of cameras, Inertial Measurement Units (IMUs), and Global Navigation Satellite System (GNSS) receivers, tightly coupled fusion frameworks, such as GVINS, have gained considerable attention. GVINS is an optimization-based factor-graph framework that integrates visual and inertial measurements with single-frequency GNSS-code pseudorange observations to provide robust and drift-free navigation. This study aimed to evaluate the potential of applying GVINS to low-cost, low-power, and single-frequency GNSS receivers, particularly those embedded in smartphones, by integrating 1 Hz GNSS measurements collected in three challenging urban scenarios into the GVINS framework to produce seamless 10 Hz positioning estimates. The experiments were conducted using an Xsens MTi-1 IMU and global-shutter (GS) cameras, as well as a Samsung A51 smartphone and a u-blox ZED-F9P GNSS receiver. GVINS was modified to process 1 Hz GNSS measurements. Differential corrections from a nearby GNSS reference station were also incorporated to assess their impact on optimization-based filters, such as GVINS. The performance of GVINS and Differential GVINS (D-GVINS) solutions using smartphone measurements was compared against standard point positioning (SPP) and differential GPS (DGPS) results obtained from the same smartphone GNSS receiver, as well as the GVINS solution derived from u-blox ZED-F9P measurements sampled at 1 Hz. Experimental results show that GVINS effectively operates with smartphone GNSS measurements, reducing 3D RMS errors by 80.4%, 64.9%, and 83.8% for the sports field, campus-walking, and campus-driving datasets, respectively, when differential corrections are applied relative to the SPP solution. These results highlight the potential of smartphone GNSS receivers within the GVINS framework: Even though they observe fewer constellations, lower signal quality, and a lower number of satellites, they can still achieve a performance comparable to that of a relatively higher-end dual-frequency GNSS receiver, the u-blox ZED-F9P. Further studies will focus on adapting the GVINS algorithm to run directly on smartphones to utilize all the available measurements, including the camera, IMU, barometer, magnetometer, and additional ranging sensors.

2007 yılında hizmete giren ve Samsun’dan Sarp sınır kapısına kadar uzanan Karadeniz Sahil Yolu (KSY), Giresun-Piraziz arasındaki bölgede bazı yerlerde karadan, bazı yerlerde deniz doldurularak inşa edilmiştir. KSY bazı yerlerde plajları daraltırken, yol kapsamında inşa edilen mahmuz şeklindeki dalgakıranların etkisiyle mevcut plajlar deniz yönünde genişlemiş, hatta yeni plajlar oluşmuştur. Mahmuz bulunmasına rağmen bazı yerlerde plaj oluşumu gerçekleşmemiş veya diğer yerlere göre daha yavaş bir oluşum söz konusudur. Kıyı boyunca farklı durumların gelişmesinde T mahmuz şeklindeki dalgakıranların ve balıkçı barınaklarının etkisi ile birlikte akarsularla sediment taşınımı, akarsu ağızlarında akarsu ile dalga etkileşimi ve özellikle deniz batimetrisi önemli parametrelerdir. Batimetrinin derin olduğu yerlerde (örneğin Piraziz sahili) ve büyük akarsu (Batlama ve Pazar Suyu) ağızlarında mahmuzlara rağmen plaj oluşumunun gerçekleşmemesi veya oluşum hızının çok yavaş olması bu iki faktörün etkisindendir. Plaj oluşum hızının en yüksek olduğu Giresun şehir plajları 2006 yılından 2020 yılına kadar alansal olarak iki buçuk kat genişlemiş ve kıyı çizgisi 60-85 m ilerlemiştir. Bu süre içinde yıllık ortalama ilerleme hızı 4,3 m ile 6 m arasında değişmektedir. Bu plajın tamamında kıyı çizgisindeki ilerleme hızı son üç-beş yıldaki gibi devam ederse en geç 2028 yılında kıyı çizgisinin mahmuz uçlarına ulaşacağı tahmin edilmektedir.

One sector that feels the effects of global warming and climate change on all levels is agriculture. In order to prepare for possible yield loss, as well as market, storage, and import planning challenges brought on by climate change, businesses can utilise agricultural decision support applications. Within the scope of this study, a crop yield prediction module has been developed that can provide in and end of season estimation of crop yields to be obtained from the determined regions. The Python programming language was used in the creation of the module as a QGIS plugin. The area for which crop yield predictions are to be made is covered by retrieving MODIS SR, MODIS LST, and Daymet data from the Google Earth Engine data catalogue. Histograms obtained from remotely sensed images are used as input data to two deep learning methods (CNN-LSTM and HistCNN). As a result, the HistCNN model outperformed CNN-LSTM for in season soybean yield prediction, with an R² of 0.72, while the CNN-LSTM model outperformed it for in end of season soybean yield prediction, with an R² of 0.67.

Terrestrial mobile gravimetry using temperature-stabilized inertial measurement units (IMUs) is emerging as an efficient technique for acquiring high-accuracy gravity data in dynamic environments. This study explores the integration of the iCORUS-2 IMU system with GNSS along the Cebeci-Elmadağ route in Ankara, Türkiye. The iTempStab technology was incorporated into the iCORUS-2 system to stabilize temperature variations to enhance the accuracy of gravity measurements. Notably, this research represents the results of vehicle-based terrestrial mobile gravimetry using a thermally stabilized IMU. An extended Kalman filter (EKF) was applied to integrate IMU and GNSS data. The advantage of terrestrial mobile gravimetry over airborne methods is the ability to make static updates for vertical gravity, zero gravity gradient and zero velocity measurements, which can be introduced into the EKF as measurement updates. The method was tested by analysing the statistical impact of these updates at varying intervals: without any gravity measurement updates, and by applying updates at approximately 2 km, 3 km, 4 km, 5 km, 6 km, and 7 km intervals. Results indicated that without updates, a root mean square error (rmse) value between external terrestrial gravity data and mobile gravimetry estimates is 1.362 mGal. However, with updates at approximately 2 km intervals, the rmse value significantly decreased to 0.948 mGal, representing a %30.4 reduction in rmse and achieving sub-mGal accuracy. The inclusion of temperature stabilization and frequent gravity measurement updates demonstrated a marked improvement in data accuracy and consistency. These advancements not only validate the potential of mobile gravimetry for geophysical surveys but also broaden its applications in fields requiring precise gravity data acquisition.