Investigation of process parameter effect on anisotropic properties of 3D printed sand molds

The development of sand mold three-dimensional printing technologies enables the manufacturing of molds without the use of a physical model. However, the effects of the three-dimensional printing process parameters on the mold permeability and strength are not well known, leading the industries to keep old settings until castings have recurring defects. In the present work, the influence of these parameters was experimentally investigated to understand their effect on the mold strength and permeability. Cylindrical and barshaped test specimens were printed to perform, respectively, permeability and bending strength measurements. Experiments were designed to statistically quantify the individual and combined effect of these process parameters. While the binder quantity only affects the mold strength, increasing the recoater speed leads to both greater permeability and reduced strength due to the reduced sand compaction. Recommendations for optimizing some 3D printer settings are proposed to attain predefined mold properties and minimize the anisotropic behavior of the sand mold in regard to both the orientation and the position in the job box

Wykorzystanie obrazu satelitarnego jako podkładu do turystycznej mapy Biebrzańskiego Parku Narodowego

Satelitarne mapy obrazowe stanowią cenny dokument łączący cechy tradycyjnych opracowań kartograficznych z bogatą treścią obrazów satelitarnych, zwłaszcza tych o podwyższonej rozdzielczości. Użycie obrazu satelitarnego jako podkładu do mapy turystycznej Biebrzańskiego Parku Narodowego czyni mapą plastyczną, żywą, pobudza wyobraźnią odbiorcy i kształtuje umiejętność kompleksowego patrzenia na środowisko przyrodnicze. Taka mapa spełnia funkcję krajoznawczą, poglądową i dydaktyczną. Dodatkowo poprzez właściwe opracowanie treści kartograficznej, wyważenie odpowiedniej ilości niezbędnych informacji może służyć szerszemu gronu odbiorców (naukowcy, turyści, uczniowie itp.), a także władzom parku w celu wywierania wpływu na wzrost intensywności ruchu turystycznego

Copernicus Program as a source of information on the dominant leaf type in Poland – assessment of the accuracy of the national high resolution layer

Information on the spatial distribution and variability of forests is important in monitoring of forest resources, biodiversity assessment, threat prevention, estimation of carbon content and forest management. The Pan−European High Resolution Layers (HRLs) produced as part of the European Earth Monitoring Programme – Copernicus provide detailed information on the land cover characteristics in Europe. The HRLs are produced using satellite imagery based on an interactive rule−based classification. There are the following HRL themes: imperviousness, forest, water and wetness and grasslands. The HRLs are available for the reference year 2012 and 2015, at the spatial resolution of 20 m. The forest related HRL consists of tree cover density, dominant tree type and forest type products. In this study, we performed a) the qualitative and quantitative analysis of the accuracy of the dominant leaf type (DLT) layer for the 2015 year at the national scale, and b) detailed analysis of the data quality at the forest stand level over the selected forest districts. The DLT layer was compared with the national orthophotos. The detailed analysis was carried out using Sentinel−2 images and forest inventory data obtained from the Forest Data Bank over the selected forest districts. The accuracy analysis of the national DLT layer revealed the high omission error equal to 18.8%, and lower commission error of 5.4%. The omission error is mostly related to the omitted orchards and young forest plantations, which are included in the DLT layer. The commission error of the broadleaved forest is related mostly to the small patches of coniferous forest that was misclassified as broadleaved. In general, commission errors were identified more frequently in broadleaved forest than in the coniferous forest. In many locations the patches of coniferous forest were misclassified as broadleaved forest. In general, the area of the broadleaved forest is overestimated

Semiautomatic land cover mapping according to the 2nd level of the CORINE Land Cover legend

Actual land cover maps are a very good source of information on present human activities. It increases value of actual spatial databases and it is a key element for decision makers. Therefore, it is important to develop fast and cheap algorithms and procedures of spatial data updating. Every day, satellite remote sensing deliver vast amount of new data, which can be semi-automatically classified. The paper presents a method of land cover classification based on a fuzzy artificial neural network simulator and Landsat TM satellite images. The latest CORINE Land Cover 2012 polygons were used as reference data. Three satellite images acquired 21 April 2011, 5 June 2010, 27 August 2011 over Warsaw and surrounding areas were processed. As an outcome of classification procedure, the maps, error matrices and a set of overall, producer and user accuracies and a kappa coefficient were achieved. The classification accuracy oscillates around 76% and confirms that artificial neural networks can be successfully used for forest, urban fabric, arable land, pastures, inland waters and permanent crops mapping. Low accuracies were obtained in case of heterogenic land cover units

The use of the artificial neural networks to update the CORINE Land Cover maps

Aktualne mapy pokrycia terenu są podstawą wielu dyscyplin nauki oraz mają szerokie zastosowanie aplikacyjne. Jednym z problemów aktualizacji map jest proces aktualizacji danych. Teledetekcja dostarcza codziennie nowych zobrazowań satelitarnych, które mogą zaspokoić potrzeby aktualizacji baz danych. W niniejszym artykule autorzy przedstawiają metodę klasyfikacji pokrycia terenu sztucznymi sieciami neuronowymi fuzzy ARTMAP zgodnie z założeniami i legendą Corine Land Cover na podstawie danych satelitarnych Landsat, które wykorzystywane są do opracowania map pokrycia terenu. W artykule użyto jako danych referencyjnych i weryfikacyjnych najnowszą mapę Corine Land Cover (CLC) 2012. Do przeprowadzenia klasyfikacji symulatorem wykorzystano trzy zdjęcia satelitarne Landsat TM (21.04.2011, 05.06.2010, 27.08.2011). Obszarem badań były okolice Warszawy. Wynikami pracy symulatora są mapy klasyfikacji pokrycia terenu oraz macierze błędów klasyfikacji. Uzyskane wyniki potwierdzają, że sztuczne sieci neuronowe mogą z powodzeniem być wykorzystywane do aktualizacji map pokrycia terenu.Modern land cover maps are the basis of many scientific disciplines and they are widely applied. One of the problems connected with the revision of maps is the data updating procedure. Remote Sensing daily provides us with the new satellite images, that can meet the needs of database updates. In this article the method of classification for land cover with the artificial, neural, fuzzy ARTMAP networks is presented by the authors in accordance with the objectives and legend of the CORINE Land Cover Map on the basis of the Landsat satellite data, which are used to elaborate the land cover maps. The latest CORINE Land Cover map 2012 polygons are used as the reference and verification data. Three satellite Landsat TM images of 21.04.2011, 05.06.2010, 27.08.2011 are processed by a fuzzy, artificial, neural network classificatory simulator. The area of research was Warsaw and its surrounding area. The results of this research are the classificatory land cover maps and error matrices. Acquired results confirm that the artificial neural networks can be successfully used for land cover updating