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نتیجه جستجو - Domain Adaptation

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring
اهرم موتور زمین گوگل و الگوریتم های یادگیری ماشین برای ترکیب در اندازه گیری درجا از زمان های مختلف برای نظارت بر مراتع-2020
Mapping and monitoring of indicators of soil cover, vegetation structure, and various native and non-native species is a critical aspect of rangeland management. With the advancement in satellite imagery as well as cloud storage and computing, the capability now exists to conduct planetary-scale analysis, including mapping of rangeland indicators. Combined with recent investments in the collection of large amounts of in situ data in the western U.S., new approaches using machine learning can enable prediction of surface conditions at times and places when no in situ data are available. However, little analysis has yet been done on how the temporal relevancy of training data influences model performance. Here, we have leveraged the Google Earth Engine (GEE) platform and a machine learning algorithm (Random Forest, after comparison with other candidates) to identify the potential impact of different sampling times (across months and years) on estimation of rangeland indicators from the Bureau of Land Managements (BLM) Assessment, Inventory, and Monitoring (AIM) and Landscape Monitoring Framework (LMF) programs. Our results indicate that temporally relevant training data improves predictions, though the training data need not be from the exact same month and year for a prediction to be temporally relevant. Moreover, inclusion of training data from the time when predictions are desired leads to lower prediction error but the addition of training data from other times does not contribute to overall model error. Using all of the available training data can lead to biases, toward the mean, for times when indicator values are especially high or low. However, for mapping purposes, limiting training data to just the time when predictions are desired can lead to poor predictions of values outside the spatial range of the training data for that period. We conclude that the best Random Forest prediction maps will use training data from all possible times with the understanding that estimates at the extremes will be biased.
Keywords: Google earth engine | Big data | Machine learning | Domain adaptation | Transfer learning | Feature selection | Rangeland monitoring
مقاله انگلیسی
2 Training My Car to See using Virtual Worlds
آموزش اتومبیل من برای دیدن با استفاده از جهان مجازی-2017
Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting in training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance.
Keywords: ADAS | Autonomous Driving | Computer Vision | Object Detection | Semantic Segmentation | Machine Learning | Data Annotation | Virtual Worlds | Domain Adaptation
مقاله انگلیسی
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