The best performing UNet model has an overall accuracy of 95% in all regions, while the Random Forest has an overall accuracy of 89%. In addition, we demonstrate the superiority of CNNs cloud predicted mapping accuracy of 81–91%, over traditional methods such as Random Forest algorithms of 57–88%. A modified UNet-like convolutional neural network (CNN) was used for the task of semantic segmentation in the regions of Vietnam, Senegal, and Ethiopia strictly using RGB + NIR spectral bands. In this study, we propose a multi-regional and multi-sensor deep learning approach for the detection of clouds in very high-resolution WorldView satellite imagery. Techniques that rely solely on the spectral information of clouds underperform in these situations. Furthermore, at this resolution, clouds can cover many thousands of pixels, making both the center and boundaries of the clouds prone to pixel contamination and variations in the spectral intensity. At very high-resolution (< 3 m), detecting clouds becomes a significant challenge due to the presence of smaller features, with spectral characteristics similar to other land cover types, and thin (partially transparent) cloud forms. At coarse spatial resolution (> 100 m), clouds are bright and generally distinguishable from other landscape surfaces. The detection of clouds is one of the first steps in the pre-processing of remotely sensed data. Given increasing global food prices and restricted commodity trade, understanding local agricultural productivity using affordable and timely remote sensing-based methods is essential for ensuring appropriate humanitarian interventions. This indicates some utility in leveraging the calibrated Random Forest models to make skillful predictions of interannual crop type and ultimately food availability of nearby communities for years with no training data. This study also showed that a model trained with high quality 2019 dry season crop cut data can predict the subsequent dry season's interannual crop type with overall accuracy as high as 60%, comparable to crop type models trained with 2020 survey data and used to estimate crop type in the concurrent season, as the survey collection. In addition, model type, linear Regression or nonlinear Random Forest, matters little when estimating yield in these landscapes, unless Harmonic regression is utilized for the linear model. However, there is a trade-off between opting for very high-resolution imagery (<2 m) or the number of bands offered by Sentinel-2 as the bands that occupy and vegetation indices that utilize the red through NIR ranges were most important across all models. Model optimization using varying spectral and vegetation index inputs can increase crop type and yield prediction accuracy in the dry season where denser cultivation is a challenge for the 10–20 m resolution of Sentinel-2. Using multi-day moderate resolution Sentinel-2 and Random Forest models, this study shows that crop type and rice yields in Burkina Faso can be predicted with greater than ∼80% accuracy in the rainy season. A particular challenge is monitoring and evaluation in regions with smallholder agricultural systems (∼1 ha) that are often subsistence focused, vulnerable to food insecurity and data scarce. Remote Sensing affords the opportunity to monitor and evaluate data scarce regions where field collection efforts are costly. Manyland is a massive multicitizen world so will require a good internet connection. and have a lot of fun together in ways none of us can predict. ![]() In an infinite, shared world of abundance, we create new things by drawing them, build new places of any kind, hang out and chat, throw around stuff, shape our own appearances, collect what we like and provide what's needed, make music, party, go swim, enjoy, jump n' run, take care of the world, do sports, come up with puzzles, explore. "The most exciting game I've played so far mainly because of its freedom to shape your world." -Cosmin "Exploring the world is a blast" -Massively It is an entire universe to explore, in constant change and full of possibility!" -Diogo "***** The only downside is that it can be very addicting!" -Tim McDonald Such a variety of things in this world to do, including things like player-made 'roller coasters' that take you for a ride!" -Joshua C "***** A WONDERFUL game, so fun to explore, build, interact, and create ART. Welcome to Manyland, an open universe we invent and live together! Welcome to Manyland, an open universe we invent and live together!
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |