The lack of official statistics makes it difficult to assess Venezuela's economic situation during the socioeconomic crisis. In this article, we used Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light images to evaluate Venezuela's crisis. The Hodrick-Prescott filter was used to decompose the sum of urban light (SUL) into two components: the sum of urban light trend (SULT) and the SUL cycle. Then, we proposed an index of nighttime light change ratio to estimate Venezuelan nighttime light dynamics. We found that Venezuela has lost 30.37% of its SULT from April 2012 to December 2018. The regression analysis shows that Venezuelan SULT had a strong relationship to a number of socioeconomic indicators: the SULT was positively correlated to crude oil production with R² of 0.9159, negatively correlated to dollar exchange rate with R² of 0.9516, and negatively correlated to the number of asylum seekers with R 2 of 0.8384. We also found that among the three states with a largest nighttime light decrease, the economy of two states was dominated by agriculture and that of one state was dominated by the oil industry. In the pixel analysis, compared with the urban cores, the suburbs of urban cores of 12 main cities had a higher percentage of SULT increased areas. Around the Venezuela-Colombia border, the SULT decreased in the Venezuelan side but increased in the Colombian side. Our analysis suggests that nighttime light imagery can help to assess Venezuela's situation during the crisis.

The Markov Random Field (MRF) energy function, constructed by existing OpenMVS-based 3D texture reconstruction algorithms, considers only the image label of the adjacent triangle face for the smoothness term and ignores the planar-structure information of the model. As a result, the generated texture charts results have too many fragments, leading to a serious local miscut and color discontinuity between texture charts. This paper fully utilizes the planar structure information of the mesh model and the visual information of the 3D triangle face on the image and proposes an improved, faster, and high-quality texture chart generation method based on the texture chart generation algorithm of the OpenMVS. This methodology of the proposed approach is as follows: (1) The visual quality on different visual images of each triangle face is scored using the visual information of the triangle face on each image in the mesh model. (2) A fully automatic Variational Shape Approximation (VSA) plane segmentation algorithm is used to segment the blocked 3D mesh models. The proposed fully automatic VSA-based plane segmentation algorithm is suitable for multi-threaded parallel processing, which solves the VSA framework needed to manually set the number of planes and the low computational efficiency in a large scene model. (3) The visual quality of the triangle face on different visual images is used as the data term, and the image label of adjective triangle and result of plane segmentation are utilized as the smoothness term to construct the MRF energy function. (4) An image label is assigned to each triangle by the minimizing energy function. A texture chart is generated by clustering the topologically-adjacent triangle faces with the same image label, and the jagged boundaries of the texture chart are smoothed. Three sets of data of different types were used for quantitative and qualitative evaluation. Compared with the original OpenMVS texture chart generation method, the experiments show that the proposed approach significantly reduces the number of texture charts, significantly improves miscuts and color differences between texture charts, and highly boosts the efficiency of VSA plane segmentation algorithm and OpenMVS texture reconstruction.

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