This article brings together the advances of multisource and multitemporal data fusion approaches with respect to the various research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references. More specifically, this work provides a bird's-eye view of many important contributions specifically dedicated to the topics of pansharpening and resolution enhancement, point cloud data fusion, hyperspectral and lidar data fusion, multitemporal data fusion, and big data and social media. In addition, the main challenges and possible future research in each area are outlined and discussed.

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to the lack of essential data and uncertainty, the epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19, and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are proposed to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for 9 days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

Landscapes in tectonically active Hindu Kush (NW Pakistan and NE Afghanistan) result from a complex integration of the effects of vertical and horizontal crustal block motions as well as erosion and deposition processes. Active tectonics in this region have greatly influenced the drainage system and geomorphic expressions. The study area is a junction of three important mountain ranges (Hindu Kush-Karakorum-Himalayas) and is thus an ideal natural laboratory to investigate the relative tectonic activity resulting from the India-Eurasia collision. We evaluate active tectonics using DEM derived drainage network and geomorphic indices hypsometric integral (HI), stream-length gradient (SL), fractal dimension (FD), basin asymmetry factor (AF), basin shape index (Bs), valley floor width to valley height ratio (Vf) and mountain front sinuosity (Smf). The results obtained from these indices were combined to yield an index of relative active tectonics (IRAT) using GIS. The average of the seven measured geomorphic indices was used to evaluate the distribution of relative tectonic activity in the study area. We defined four classes to define the degree of relative tectonic activity: class 1__very high (1.0 ≤ IRAT < 1.3); class 2__high (1.3 ≥ IRAT < 1.5); class 3—moderate (1.5 ≥ IRAT < 1.8); and class 4—low (1.8 ≥ IRAT). In view of the results, we conclude that this combined approach allows the identification of the highly deformed areas related to active tectonics. Landsat imagery and field observations also evidence the presence of active tectonics based on the deflected streams, deformed landforms, active mountain fronts and triangular facets. The indicative values of IRAT are consistent with the areas of known relative uplift rates, landforms and geology