Jagannath Aryal

TL;DR: The CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner, Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the results of augmentation strategies to artificially increase the number of existing samples are better understanding.

TL;DR: In this article, the authors developed a methodology using object-oriented classification techniques and very high-resolution multispectral Ikonos imagery to automatically map the extent, distribution and density of private gardens in the city of Dunedin, New Zealand.

TL;DR: This approach does not provide maps as detailed as those produced by manually interpreting aerial photographs, but it can still extract ecologically significant classes and is an efficient way to generate accurate and detailed maps in significantly shorter time.

TL;DR: An ensemble method based on a two-layered machine learning model is developed to establish relationship between fire incidence and climatic data and demonstrates that the model provides highly accurate bush-fire incidence hot-spot estimation from the weekly climatic surfaces.

TL;DR: A methodology that incorporates object-based image analysis with three machine learning methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal.