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Kurzzeitvermietungen in der EU.

Timely and accurate cropland information at large spatial scales can improve crop management and support the government in decision making. Mapping the spatial extent and distribution of crops on a large spatial scale is challenging work due to the spatial variability. A multi-task spatiotemporal deep learning model, named LSTM-MTL, was developed in this study for large-scale rice mapping by utilizing time-series Sentinel-1 SAR data. The model showed a reasonable rice classification accuracy in the major rice production areas of the U.S. (OA = 98.3%, F1 score = 0.804), even when it only utilized SAR data. The model learned region-specific and common features simultaneously, and yielded a significant improved performance compared with RF and AtBiLSTM in both global and local training scenarios. We found that the LSTM-MTL model achieved a regional F1 score up to 10% higher than both global and local baseline models. The results demonstrated that the consideration of spatial variability via LSTM-MTL approach yielded an improved crop classification performance at a large spatial scale. We analyzed the input-output relationship through gradient backpropagation and found that low VH value in the early period and high VH value in the latter period were critical for rice classification. The results of in-season analysis showed that the model was able to yield a high accuracy (F1 score = 0.746) two months before rice maturity. The integration between multi-task learning and multi-temporal deep learning approach provides a promising approach for crop mapping at large spatial scales.

Hotels and Short-Term rental groups are typically always on opposite sides of the political isle when lobbying on what technically counts as a short-term rental. However, in the post-pandemic era, they can both learn from one another. Hotels do not need and simply cannot, given the labor shortage, to outsource and entire team to welcome guests to their rooms. Adversely, short-term rentals, like Airbnb, can do a much better job at communicating with their guests. Contactless communication can be streamlined through and app that most hotels traded front desk interactions for given the pandemic. Airbnb will need to streamline their app usage to create less back-and-forth between both parties

This note is a sequel to an earlier article by W. A. B. Hopkin and W. A. H. Godley which set out a method of estimating how the main aggregates in the national accounts will be affected over a period of 18 months or two years by changes in taxation. More recent experience and additional research into methods of short-term economic forecasting have led to further developments in the same field.