SUT-Crack

The SUT-Crack dataset contains high-quality images of asphalt pavement cracks, specifically curated for crack detection using deep learning techniques like classification, object detection, and segmentation. Careful consideration was given during dataset creation to encompass various crack detection challenges, such as oil stains, shadows, and different lighting conditions. The images were captured from a fixed height of 672 mm above the pavement surface, facilitating calibration for real-world crack length measurements. Notably, the dataset also includes geotags, providing precise latitude and longitude coordinates for each image.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

SUT-Crack

The SUT-Crack dataset contains high-quality images of asphalt pavement cracks, specifically curated for crack detection using deep learning techniques like classification, object detection, and segmentation. Careful consideration was given during dataset creation to encompass various crack detection challenges, such as oil stains, shadows, and different lighting conditions. The images were captured from a fixed height of 672 mm above the pavement surface, facilitating calibration for real-world crack length measurements. Notably, the dataset also includes geotags, providing precise latitude and longitude coordinates for each image.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

SUT-Crack

The SUT-Crack dataset contains high-quality images of asphalt pavement cracks, specifically curated for crack detection using deep learning techniques like classification, object detection, and segmentation. Careful consideration was given during dataset creation to encompass various crack detection challenges, such as oil stains, shadows, and different lighting conditions. The images were captured from a fixed height of 672 mm above the pavement surface, facilitating calibration for real-world crack length measurements. Notably, the dataset also includes geotags, providing precise latitude and longitude coordinates for each image.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

SUT-Crack

The SUT-Crack dataset contains high-quality images of asphalt pavement cracks, specifically curated for crack detection using deep learning techniques like classification, object detection, and segmentation. Careful consideration was given during dataset creation to encompass various crack detection challenges, such as oil stains, shadows, and different lighting conditions. The images were captured from a fixed height of 672 mm above the pavement surface, facilitating calibration for real-world crack length measurements. Notably, the dataset also includes geotags, providing precise latitude and longitude coordinates for each image.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

SUT-Crack

The SUT-Crack dataset contains high-quality images of asphalt pavement cracks, specifically curated for crack detection using deep learning techniques like classification, object detection, and segmentation. Careful consideration was given during dataset creation to encompass various crack detection challenges, such as oil stains, shadows, and different lighting conditions. The images were captured from a fixed height of 672 mm above the pavement surface, facilitating calibration for real-world crack length measurements. Notably, the dataset also includes geotags, providing precise latitude and longitude coordinates for each image.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Mineral chemistry, Thermo-barometry and Crystal Size Distribution of volcanic rocks from Shirinak: Implication for genesis of volcanic rocks in the southeast of Urumieh-Dokhtar (Kerman province)

The Shirinak volcanic rocks, known as Dahaj-Sarduieh belt in Kerman province, are exposed southeast of Urumieh-Dokhtar volcanic belt. Petrographically, the volcanic rocks are basalts and andesite, which consist mainly of plagioclase, clinopyroxene, olivine as well as calcite, quartz and chlorite as the secondary minerals.  All of these minerals set in fine grain matrix with porphyric and glomeroporphyric textures. Based on mineral chemistry data, plagioclases range from labradorite to bytownite and have been undergone compositional and thermal mixing. They mostly show sieve texture.  CSD (crystal size distribution) study shows that the shape of plagioclase microlites is tablet with aspect ratio of 1:7:10 for short:intermediate:long axes, respectively. Moreover, three-dimensional shape of plagioclase crystals, nucleation and growth time were estimated 40.27 years, which is completely consistent with the nature of basalt. Based on dip of CSD diagram, magma mixing process has been clearly involved in the magma genesis. The pyroxenes studied are augite in composition that were physically crystalized in moderate to high pressure and temperature of 550-1110 ̊ C. They crystallized from a magma likely with about 10% fluid and in variable fO2 condition. On the base of pyroxene chemistry, the basic rocks from Shirinak belong to tholeiitic to calcalkaline series in volcanic arc setting (Neo-Tethys subduction)

INTEGRATION OF DUAL BORDER EFFECTS IN RESOURCE ESTIMATION: A COKRIGING PRACTICE ON A COPPER PORPHYRY DEPOSIT

Hierarchical or cascade resource estimation is a very common practice when building a geological block model in metalliferous deposits. One option for this is to model the geological domains by indicator kriging and then to estimate (by kriging) the grade of interest within the built geodomains. There are three problems regarding this. The first is that sometimes the molded geological domains are spotty and fragmented and, thus, far from the geological interpretation. The second is that the resulting estimated grades highly suffer from a smoothing effect. The third is related to the border effect of the continuous variable across the boundary of geological domains. The latter means that the final block model of the grade shows a very abrupt transition when crossing the border of two adjacent geological domains. This characteristic of the border effect may not be always true, and it is plausible that some of the variables show smooth or soft boundaries. The case is even more complicated when there is a mixture of hard and soft boundaries. A solution is provided in this paper to employ a cokriging paradigm for jointly modeling grade and geological domains. The results of modeling the copper in an Iranian copper porphyry deposit through the proposed approach illustrates that the method is not only capable of handling the mixture of hard and soft boundaries, but it also produces models that are less influenced by the smoothing effect. These results are compared to an independent kriging, where each variable is modeled separately, irrespective of the influence of geological domain

SUT-Crack

The SUT-Crack dataset contains high-quality images of asphalt pavement cracks, specifically curated for crack detection using deep learning techniques like classification, object detection, and segmentation. Careful consideration was given during dataset creation to encompass various crack detection challenges, such as oil stains, shadows, and different lighting conditions. The images were captured from a fixed height of 672 mm above the pavement surface, facilitating calibration for real-world crack length measurements. Notably, the dataset also includes geotags, providing precise latitude and longitude coordinates for each image.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV