Ocjena točnosti različitih metoda strojnog učenja na satelitskim snimkama RapidEye i PlanetScope

Since the first satellite imagery of RapidEye and PlanetScope became available, numerous studies have been conducted. However, only a few authors have focused on evaluating the accuracy of more than two machine learning methods in land cover classification. This paper evaluates the accuracy of four different machine learning methods, namely: support vector machine, artificial neural network, naive Bayes, and random forest. All analysis was conducted on cities in Croatia, Varaždin and Osijek. On Varaždin area on RapidEye satellite imagery support vector machine achieved overall kappa value 0.80, artificial neural network 0.37, naive Bayes 0.84 and random forest 0.76. On Varaždin area on PlanetScope satellite imagery support vector machine achieved overall kappa value 0.77, artificial neural network 0.38, naive Bayes 0.76 and random forest 0.75. On Osijek area on RapidEye satellite imagery support vector machine achieved overall kappa value 0.75, artificial neural network 0.36, naive Bayes 0.85 and random forest 0.76. On Osijek area on PlanetScope satellite imagery support vector machine achieved overall kappa value 0.64, artificial neural network 0.23, naive Bayes 0.72 and random forest 0.63. Performance time of each method is also evaluated. Naive Bayes and random forest have best performance time in every scenario.Otkako su prve satelitske snimke senzora RapidEye i PlanetScope postale dostupne, na njima su provedena brojna istraživanja. Međutim, samo se nekoliko autora usredotočilo na ocjenu točnosti više od dvije metode strojnog učenja pri klasifikaciji pokrova zemljišta. U ovom radu daje se ocjena točnosti četiri različite metode strojnog učenja: metode potpornih vektora, metode umjetnih neuronskih mreža, metode naivni Bayes i metode slučajnog šuma. Sve su analize provedene na gradovima u Hrvatskoj: Varaždinu i Osijeku. Na satelitskom snimku senzora RapidEye, za područje Varaždina, metoda potpornih vektora postigla je ukupnu kappa vrijednost 0,80, metoda umjetnih neuronskih mreža 0,37, metoda naivni Bayes 0,84 i metoda slučajnog šuma 0,76. Na satelitskom snimku senzora PlanetScope, za područje Varaždina, metoda potpornih vektora postigla je ukupnu kappa vrijednost 0,77, metoda umjetnih neuronskih mreža 0,38, metoda naivni Bayes 0,76 i metoda slučajnog šuma 0,75. Na satelitskom snimku senzora RapidEye, za područje Osijeka, metoda potpornih vektora postigla je ukupnu kappa vrijednost 0,75, metoda umjetnih neuronskih mreža 0,36, metoda naivni Bayes 0,85 i metoda slučajnog šuma 0,76. Na satelitskom snimku senzora PlanetScope, za područje Osijeka, metoda potpornih vektora postigla je ukupnu kappa vrijednost 0,64, metoda umjetnih neuronskih mreža 0,23, metoda naivni Bayes 0,72 i metoda slučajnog šuma 0,63. U radu se također mjeri i vrijeme izvedbe svake metode. Metoda naivni Bayes i metoda slučajnog šuma imaju najbolje vrijeme izvedbe u svim slučajevima

Web GIS for Airport Emergency Response - UML Model

The main objective of integrating Web GIS in airport emergency response should be to provide the most appropriate geospatial information to all participants. Airport emergency response still needs a model that will explain its complexity: its participants, their tasks and information needs. This paper presents the UML model of airport emergency response. Such a model facilitates a common understanding of the system by participants coming from airport, police, fire brigade, etc. It also enables institutional agreements for sharing data. The developers have got specifications of geospatial data and GIS functions imposed by participants and standards. A prototype Web GIS application is developed and presented to the users for evaluation. The prototype has shown how GIS functions can improve airport emergency response. The users have shown great interest, and they have great expectations in further integration of Web GIS in airport emergency response

Mario Miler, PhD in Technical Sciences

Mario Miler obranio je 28. travnja 2014. na Geodetskom fakultetu Sveučilišta u Zagrebu doktorski rad Implementacija geoprostornoga modela u nerelacijske baze podataka. Doktorski rad obranjen je pred povjerenstvom u sastavu doc. dr. sc. Dubravko Gajski, prof. dr. sc. Drago Špoljarić i dr. sc. Miroslav Slamić, prof. visoke škole s Tehničkog veleučilišta u Zagrebu. Mentor je bio prof. dr. sc. Damir Medak.Mario Miler defended his doctoral thesis Implementation of Geospatial Data Model in Non-Relational Databases at the University of Zagreb, Faculty of Geodesy on April 28, 2014. The Committee for Defence included Assist. Prof. Dr. Dubravko Gajski, Prof. Dr. Drago Špoljarić and Dr. Miroslav Slamić from the Polytechnic of Zagreb. Prof. Dr. Damir Medak was the mentor

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Dvogodišnja TEMPUS CARDS stipendija za Geodetski fakultet Sveučilišta u Zagrebu

Dana je vijest o dvogodišnjoj TEMPUS CARDS stipendiji za Geodetski fakultet Sveučilišta u Zagrebu