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The large scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such as the Landsat and Sentinel series. More recently, researchers have explored classification and regression techniques to model impervious surfaces. However, these modeling efforts are limited due to lack of labeled data for training and evaluation. This in turn requires significant effort for manual labeling of data and visual interpretation of results. In this paper, we train deep learning neural networks using TensorFlow to predict impervious surfaces from Landsat 8 images. We used OpenStreetMap (OSM), a crowd-sourced map of the world with manually interpreted impervious surfaces such as roads and buildings, to programmatically generate large amounts of training and evaluation data, thus overcoming the need for manual labeling. We conducted extensive experimentation to compare the performance of different deep learning neural network architectures, optimization methods, and the set of features used to train the networks. The four model configurations labeled U-Net_SGD_Bands, U-Net_Adam_Bands, U-Net_Adam_Bands+SI, and VGG-19_Adam_Bands+SI resulted in a root mean squared error (RMSE) of 0.1582, 0.1358, 0.1375, and 0.1582 and an accuracy of 90.87%, 92.28%, 92.46%, and 90.11%, respectively, on the test set. The U-Net_Adam_Bands+SI Model, similar to the others mentioned above, is a deep learning neural network that combines Landsat 8 bands with statistical indices. This model performs the best among all four on statistical accuracy and produces qualitatively sharper and brighter predictions of impervious surfaces as compared to the other models.
Jash Parekh; Ate Poortinga; Biplov Bhandari; Timothy Mayer; David Saah; Farrukh Chishtie. Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning. Remote Sensing 2021, 13, 3166 .
AMA StyleJash Parekh, Ate Poortinga, Biplov Bhandari, Timothy Mayer, David Saah, Farrukh Chishtie. Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning. Remote Sensing. 2021; 13 (16):3166.
Chicago/Turabian StyleJash Parekh; Ate Poortinga; Biplov Bhandari; Timothy Mayer; David Saah; Farrukh Chishtie. 2021. "Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning." Remote Sensing 13, no. 16: 3166.
Declining populations of Zizania palustris L. (northern wildrice, or wildrice) during the last century drives the demand for new and innovative techniques to support monitoring of this culturally and ecologically significant crop wild relative. We trained three wildrice detection models in R and Google Earth Engine using data from annual aquatic vegetation surveys in northern Minnesota. Three different training datasets, varying in the definition of wildrice presence, were combined with Landsat 8 Operational Land Imager (OLI) and Sentinel-1 C-band synthetic aperture radar (SAR) imagery to map wildrice in 2015 using random forests. Spectral predictors were derived from phenologically important time periods of emergence (June–July) and peak harvest (August–September). The range of the Vertical Vertical (VV) polarization between the two time periods was consistently the top predictor. Model outputs were evaluated using both point and area-based validation (polygon). While all models performed well in the point validation with percent correctly classified ranging from 83.8% to 91.1%, we found polygon validation necessary to comprehensively assess wildrice detection accuracy. Our practical approach highlights a variety of applications that can be applied to guide field excursions and estimate the extent of occurrence at landscape scales. Further testing and validation of the methods we present may support multiyear monitoring which is foundational for the preservation of wildrice for future generations.
Kristen O’Shea; Jillian LaRoe; Anthony Vorster; Nicholas Young; Paul Evangelista; Timothy Mayer; Daniel Carver; Eli Simonson; Vanesa Martin; Paul Radomski; Joshua Knopik; Anthony Kern; Colin K. Khoury. Improved Remote Sensing Methods to Detect Northern Wild Rice (Zizania palustris L.). Remote Sensing 2020, 12, 3023 .
AMA StyleKristen O’Shea, Jillian LaRoe, Anthony Vorster, Nicholas Young, Paul Evangelista, Timothy Mayer, Daniel Carver, Eli Simonson, Vanesa Martin, Paul Radomski, Joshua Knopik, Anthony Kern, Colin K. Khoury. Improved Remote Sensing Methods to Detect Northern Wild Rice (Zizania palustris L.). Remote Sensing. 2020; 12 (18):3023.
Chicago/Turabian StyleKristen O’Shea; Jillian LaRoe; Anthony Vorster; Nicholas Young; Paul Evangelista; Timothy Mayer; Daniel Carver; Eli Simonson; Vanesa Martin; Paul Radomski; Joshua Knopik; Anthony Kern; Colin K. Khoury. 2020. "Improved Remote Sensing Methods to Detect Northern Wild Rice (Zizania palustris L.)." Remote Sensing 12, no. 18: 3023.