Advanced Cyberinfrastructure for Agricultural Drought Monitoring

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Fang

et al. 2020

Self Cite

Recently, agricultural remote sensing community has endeavored to utilize the power of artificial intelligence (AI). One important topic is using AI to make the mapping of crops more accurate, automatic, and rapid. This article proposed a classification workflow using deep neural network (DNN) to produce high-quality in-season crop maps from Landsat imageries for North Dakota. We use historical crop maps from the agricultural department and North Dakota ground measurements as training datasets. Processing workflows are created to automate the tedious preprocessing, training, testing, and postprocessing workflows. We tested this hybrid solution on new images and received accurate results on major crops such as corn, soybean, barley, spring wheat, dry beans, sugar beets, and alfalfa. The pixelwise overall accuracy in all three test regions is over 82% for all land types (including noncrop land), which is the same level of accuracy as the U.S. Department of Agriculture Cropland Data Layer. The texture of DNN maps is more consistent with fewer noises, which is more comfortable to read. We find DNN is better on recognizing big farmlands than recognizing the scattered wetlands and suburban regions in North Dakota. The model trained on multiple scenes of multiple years and months yields higher accuracy than any of the models trained only on a single scene, a single month, or a single year. These results reflect that DNN can produce reliable in-season maps for major crops in North Dakota big farms and could provide a relatively accurate reference for the minor crops in scattered wetland fields.

Section: Setup On Advanced Workflow Systemmentioning

confidence: 99%

Deep Learning Classification for Crop Types in North Dakota

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Fang

et al. 2020

Self Cite

“…We experimented Geoweaver by our current research of using Landsat images and deep learning classification algorithms to study land cover changes and corresponding socio-economic influences [3,5,[102][103][104][105][106][107]. The AI workflow has already been published in the work of [5].…”

Section: Use Case: Ai-based Agriculture Mappingmentioning

confidence: 99%

“…We leveraged cloud computing (GeoBrain Cloud) [110][111][112] and parallel processing (NVIDIA CUDA GPU) [93,113] to meet the challenge of processing tremendous number of pixels in remote sensing images (a single Landsat 8 scene contains more than 34 million pixels) [114]. The classified maps will help agriculture monitoring, prediction, and decision making [107,115]. The entire experiment poses too many management issues for scientists to handle.…”

Section: Use Case: Ai-based Agriculture Mappingmentioning

confidence: 99%

Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows

Burgess

et al. 2020

IJGI

Self Cite

AI (artificial intelligence)-based analysis of geospatial data has gained a lot of attention. Geospatial datasets are multi-dimensional; have spatiotemporal context; exist in disparate formats; and require sophisticated AI workflows that include not only the AI algorithm training and testing, but also data preprocessing and result post-processing. This complexity poses a huge challenge when it comes to full-stack AI workflow management, as researchers often use an assortment of time-intensive manual operations to manage their projects. However, none of the existing workflow management software provides a satisfying solution on hybrid resources, full file access, data flow, code control, and provenance. This paper introduces a new system named Geoweaver to improve the efficiency of full-stack AI workflow management. It supports linking all the preprocessing, AI training and testing, and post-processing steps into a single automated workflow. To demonstrate its utility, we present a use case in which Geoweaver manages end-to-end deep learning for in-time crop mapping using Landsat data. We show how Geoweaver effectively removes the tedium of managing various scripts, code, libraries, Jupyter Notebooks, datasets, servers, and platforms, greatly reducing the time, cost, and effort researchers must spend on such AI-based workflows. The concepts demonstrated through Geoweaver serve as an important building block in the future of cyberinfrastructure for AI research.

“…It measures the greenness of the vegetation and the higher values mean the vegetation is greener. Greenness is highly correlated to vegetation health so many health indices are created based on NDVI, such as VCI (Vegetation Condition Index) [62][63][64] and VHI (Vegetation Health Index) [65]. As the indices are calculated based on remote sensing images that have serious gaps caused by clouds and shadows, there are big blank regions in the images.…”

Section: Demonstrationmentioning

confidence: 99%

“…For the CDL product, GeoFairy2 reuses web services in CropScape [67]. The other land cover products are served on the information proxy server in GeoBrainCloud [49,64]. The CDL has more classes than other land cover products and gives more detailed records on crops.…”

Section: Demonstrationmentioning

confidence: 99%

GeoFairy2: A Cross-Institution Mobile Gateway to Location-Linked Data for In-Situ Decision Making

Cvetojevic

et al. 2020

IJGI

Self Cite

To effectively disseminate location-linked information despite the existence of digital walls across institutions, this study developed a cross-institution mobile App, named GeoFairy2, to overcome the virtual gaps among multi-source datasets and aid the general users to make thorough accurate in-situ decisions. The app provides a one-stop service with relevant information to assist with instant decision making. It was tested and proven to be capable of on-demand coupling and delivering location-based information from multiple sources. The app can help general users to crack down the digital walls among information pools and serve as a one-stop retrieval place for all information. GeoFairy2 was experimented with to gather real-time and historical information about crops, soil, water, and climate. Instead of a one-way data portal, GeoFairy2 allows general users to submit photos and observations to support citizen science projects and derive new insights, and further refine the future service. The two-directional mechanism makes GeoFairy2 a useful mobile gateway to access and contribute to the rapidly growing, heterogeneous, multisource, and location-linked datasets, and pave a way to drive us into a new mobile web with more links and less digital walls across data providers and institutions.