Gönüllü Coğrafi Bilgi (GCB), sivil bilimin konum temelli bir türüdür ve son zamanlarda veri toplama alanında hızla gelişmekte olan bir alan haline gelmiştir. Her ne kadar güncel ve gelişmekte olan bir araştırma alanı olsa da ve gelişmiş ülkelerin çoğunda başarılı çalışmalar yürütülmüş olsa da, GCB Türkiye'de çok yeni bir araştırma alanıdır ve uygulamaları hala sınırlıdır. Bu nedenle, çalışma bağlamında GCB ile ilgili araştırmalar özetlenecek ve GCB’ ye dayalı kullanıcı yetkili kurumlar arasındaki iletişimi artıracak bir sosyal sorumluluk mobil uygulamanın geliştirilmesi aşamaları anlatılacaktır. Mobil uygulamada, kullanıcılar tüm vatandaşlardır. Yetkili kurumlar ise belediyeler, polis müdürlükleri vb. kamu kurumlarıdır. Mobil uygulama sayesinde kullanıcılar, toplanmamış çöp, yanlış park edilmiş bir araç veya araçlara zarar verebilecek bir çukur gibi kentsel sorunların fotoğraflarını, konumlarını ve açıklamalarını ekleyerek sisteme yükleyebilirler. Sisteme yüklenen kentsel sorun, ilgili kurum tarafından görülmekte ve bundan sonra kurum, kullanıcıları bilgilendirmek amacıyla sorunu çözmek için yapılan işe ait açıklamalar, fotoğraflar gibi bilgileri paylaşmaktadır. Uygulama aynı zamanda açık sosyal medya mobil uygulaması olduğundan, tüm kullanıcılar paylaşımı ve geri bildirimleri görebilecektir.

Generation of orthoimages in remote sensing and photogrammetry is a common and sometimes mandatory issue. The desired georeferencing accuracy of the orthoimage depends on two main factors: accuracy of transformation parameters and density and accuracy of height information. The objective of this letter is to estimate the combined efficiency of accuracies of refined rational polynomial coefficients and digital elevation models on georeferencing accuracy of the orthoimage utilizing the figure condition method (FCM). In this letter, details of the method, the characteristics of the evaluated Pléiades panchromatic mono image and auxiliary data, and also the test site are presented. The findings were compared with other results from high-resolution satellite images such as those from IKONOS, QuickBird, and OrbView-3. It is observed that the FCM might be preferred to estimate the overall georeferencing accuracy of an orthoimage, reaching ± 0.3-2.7-pixel accuracy range. The Pléiades panchromatic mono image presents almost equal accuracy as the compared images. The research was carried out on the Zonguldak (Turkey) test site which has mountainous and extremely undulating topography.

Pléiades 1A and 1B are twin optical satellites of Optical and Radar Federated Earth Observation (ORFEO) program jointly running by France and Italy. They are the first satellites of Europe with sub-meter resolution. Airbus DS (formerly Astrium Geo) runs a MyGIC (formerly Pléiades Users Group) program to validate Pléiades images worldwide for various application purposes. The authors conduct three projects, one is within this program, the second is supported by BEU Scientific Research Project Program, and the third is supported by TÜBİTAK. Assessment of georeferencing accuracy, image quality, pansharpening performance and Digital Surface Model/Digital Terrain Model (DSM/DTM) quality subjects are investigated in these projects. For these purposes, triplet panchromatic (50 cm Ground Sampling Distance (GSD)) and VNIR (2 m GSD) Pléiades 1A images were investigated over Zonguldak test site (Turkey) which is urbanised, mountainous and covered by dense forest. The georeferencing accuracy was estimated with a standard deviation in X and Y (SX, SY) in the range of 0.45m by bias corrected Rational Polynomial Coefficient (RPC) orientation, using ~170 Ground Control Points (GCPs). 3D standard deviation of ±0.44m in X, ±0.51m in Y, and ±1.82m in Z directions have been reached in spite of the very narrow angle of convergence by bias corrected RPC orientation. The image quality was also investigated with respect to effective resolution, Signal to Noise Ratio (SNR) and blur coefficient. The effective resolution was estimated with factor slightly below 1.0, meaning that the image quality corresponds to the nominal resolution of 50cm. The blur coefficients were achieved between 0.39-0.46 for triplet panchromatic images, indicating a satisfying image quality. SNR is in the range of other comparable space borne images which may be caused by de-noising of Pléiades images. The pansharpened images were generated by various methods, and are validated by most common statistical metrics and also visual interpretation. The generated DSM and DTM were achieved with ±1.6m standard deviation in Z (SZ) in relation to a reference DTM.

Reliable spatial estimation of extreme drought intensity represented by the minimum SPI value is essential for water resources management under increasing climate variability. However, conventional assessments often rely on station-based temporal indices, providing limited representation of spatial heterogeneity and topographic controls. This study developed a machine learning framework to estimate the spatial distribution of extreme drought intensity represented by the minimum SPI value across Türkiye using long-term precipitation records from 102 meteorological stations. For each station and SPI accumulation period, extreme drought intensity was represented by the minimum (most negative) SPI value observed over the 1935–2024 record; this minimum SPI value was used as the machine-learning target, while latitude, longitude, and elevation were used as geographic predictors. Seven machine learning models were evaluated; among them, GBM yielded the highest accuracy (R ² = 0.991, RMSE = 0.007), followed by XGBoost and CatBoost. These results indicate that ensemble tree-based methods reproduce the observed spatial pattern of extreme drought intensity across the station network with high accuracy; establishing predictive skill at unmonitored locations would require evaluation under a station-grouped partition. Furthermore, feature importance and SHAP analyses showed that spatial predictors (longitude, latitude, and elevation) were substantially more influential than the time-scale variable, although their relative importance varied among the evaluated models. This framework offers a robust tool for interpretable and spatially explicit drought monitoring in topographically complex regions.