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1. Improve Learning from Crowds via Generative Augmentation

   https://doi.org/10.1145/3447548.3467409  

   Zhendong, Chu; Hongning, Wang (August 2021, Proceedings of the 27th ACM SIGKDDConference on Knowledge Discovery and Data Mining (KDD ’21))

   null (Ed.)

   Crowdsourcing provides an efficient label collection schema for supervised machine learning. However, to control annotation cost, each instance in the crowdsourced data is typically annotated by a small number of annotators. This creates a sparsity issue and limits the quality of machine learning models trained on such data. In this paper, we study how to handle sparsity in crowdsourced data using data augmentation. Specifically, we propose to directly learn a classifier by augmenting the raw sparse annotations. We implement two principles of high-quality augmentation using Generative Adversarial Networks: 1) the generated annotations should follow the distribution of authentic ones, which is measured by a discriminator; 2) the generated annotations should have high mutual information with the ground-truth labels, which is measured by an auxiliary network. Extensive experiments and comparisons against an array of state-of-the-art learning from crowds methods on three real-world datasets proved the effectiveness of our data augmentation framework. It shows the potential of our algorithm for low-budget crowdsourcing in general. 

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2. Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model

   https://doi.org/10.3390/ijgi10030144  

   Gebrehiwot, Asmamaw A; Hashemi-Beni, Leila (March 2021, ISPRS International Journal of Geo-Information)

   null (Ed.)

   Flood occurrence is increasing due to the expansion of urbanization and extreme weather like hurricanes; hence, research on methods of inundation monitoring and mapping has increased to reduce the severe impacts of flood disasters. This research studies and compares two methods for inundation depth estimation using UAV images and topographic data. The methods consist of three main stages: (1) extracting flooded areas and create 2D inundation polygons using deep learning; (2) reconstructing 3D water surface using the polygons and topographic data; and (3) deriving a water depth map using the 3D reconstructed water surface and a pre-flood DEM. The two methods are different at reconstructing the 3D water surface (stage 2). The first method uses structure from motion (SfM) for creating a point cloud of the area from overlapping UAV images, and the water polygons resulted from stage 1 is applied for water point cloud classification. While the second method reconstructs the water surface by intersecting the water polygons and a pre-flood DEM created using the pre-flood LiDAR data. We evaluate the proposed methods for inundation depth mapping over the Town of Princeville during a flooding event during Hurricane Matthew. The methods are compared and validated using the USGS gauge water level data acquired during the flood event. The RMSEs for water depth using the SfM method and integrated method based on deep learning and DEM were 0.34m and 0.26m, respectively. 

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3. Machine truth serum: a surprisingly popular approach to improving ensemble methods

   https://doi.org/10.1007/s10994-022-06183-y  

   Luo, Tianyi; Liu, Yang (January 2022, Machine Learning)

   Wisdom of the crowd (Surowiecki, 2005a) disclosed a striking fact that the majority voting answer from a crowd is usually more accurate than a few individual experts. The same story is observed in machine learning - ensemble methods (Dietterich, 2000) leverage this idea to exploit multiple machine learning algorithms in various settings e.g., supervised learning and semi-supervised learning to achieve better performance by aggregating the predictions of different algorithms than that obtained from any constituent algorithm alone. Nonetheless, the existing aggregating rule would fail when the majority answer of all the constituent algorithms is more likely to be wrong. In this paper, we extend the idea proposed in Bayesian Truth Serum (Prelec, 2004) that “a surprisingly more popular answer is more likely to be the true answer instead of the majority one” to supervised classification further improved by ensemble final predictions method and semi-supervised classification (e.g., MixMatch (Berthelot et al., 2019)) enhanced by ensemble data augmentations method. The challenge for us is to define or detect when an answer should be considered as being “surprising”. We present two machine learning aided methods which can reveal the truth when the minority instead of majority has the true answer on both settings of supervised and semi-supervised classification problems. We name our proposed method the Machine Truth Serum. Our experiments on a set of classification tasks (image, text, etc.) show that the classification performance can be further improved by applying Machine Truth Serum in the ensemble final predictions step (supervised) and in the ensemble data augmentations step (semi-supervised). 

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4. Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

   https://doi.org/10.3390/rs13101966  

   Smith, Christopher W; Panda, Santosh K; Bhatt, Uma S; Meyer, Franz J; Badola, Anushree; Hrobak, Jennifer L (May 2021, Remote Sensing)

   null (Ed.)

   In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent. 

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5. Sugarcane Mosaic Virus Detection in Maize Using UAS Multispectral Imagery

   https://doi.org/10.3390/rs16173296  

   Bevers, Noah; Ohlson, Erik W; KC, Kushal; Jones, Mark W; Khanal, Sami (September 2024, Remote Sensing)

   One of the most important and widespread corn/maize virus diseases is maize dwarf mosaic (MDM), which can be induced by sugarcane mosaic virus (SCMV). This study explores a machine learning analysis of five-band multispectral imagery collected via an unmanned aerial system (UAS) during the 2021 and 2022 seasons for SCMV disease detection in corn fields. The three primary objectives are to (i) determine the spectral bands and vegetation indices that are most important or correlated with SCMV infection in corn, (ii) compare spectral signatures of mock-inoculated and SCMV-inoculated plants, and (iii) compare the performance of four machine learning algorithms, including ridge regression, support vector machine (SVM), random forest, and XGBoost, in predicting SCMV during early and late stages in corn. On average, SCMV-inoculated plants had higher reflectance values for blue, green, red, and red-edge bands and lower reflectance for near-infrared as compared to mock-inoculated samples. Across both years, the XGBoost regression model performed best for predicting disease incidence percentage (R2 = 0.29, RMSE = 29.26), and SVM classification performed best for the binary prediction of SCMV-inoculated vs. mock-inoculated samples (72.9% accuracy). Generally, model performances appeared to increase as the season progressed into August and September. According to Shapley additive explanations (SHAP analysis) of the top performing models, the simplified canopy chlorophyll content index (SCCCI) and saturation index (SI) were the vegetation indices that consistently had the strongest impacts on model behavior for SCMV disease regression and classification prediction. The findings of this study demonstrate the potential for the development of UAS image-based tools for farmers, aiming to facilitate the precise identification and mapping of SCMV infection in corn. 

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