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Articles 1 - 9 of 9
Full-Text Articles in Life Sciences
Object-Detection From Multi-View Remote Sensing Images: A Case Study Of Fruit And Flower Detection And Counting On A Central Florida Strawberry Farm, Caiwang Zheng, Tao Liu, Amr Abd-Elrahman, Vance M. Whitaker, Benjamin Wilkinson
Object-Detection From Multi-View Remote Sensing Images: A Case Study Of Fruit And Flower Detection And Counting On A Central Florida Strawberry Farm, Caiwang Zheng, Tao Liu, Amr Abd-Elrahman, Vance M. Whitaker, Benjamin Wilkinson
Michigan Tech Publications, Part 2
Object detection in remote sensing images is one of the most critical computer vision tasks for various earth observation applications. Previous studies applied object detection models to orthomosaic images generated from the SfM (Structure-from-Motion) analysis to perform object detection and counting. However, some small objects that are occluded from the vertical view but observable in raw images from the oblique views cannot be detected in the orthomosaic image, leading to an occlusion issue that cannot be resolved with the traditional orthophoto-based approach. Taking strawberry detection as a case study, the objective of this study is to detect small objects directly …
Mississippi Sky Conditions, Joby Czarnecki, Sathishkumar Samiappan, Louis Wasson, C. Daniel Mccraine
Mississippi Sky Conditions, Joby Czarnecki, Sathishkumar Samiappan, Louis Wasson, C. Daniel Mccraine
College of Agriculture & Life Sciences Publications and Scholarship
This dataset consists of approximately 13,000 jpg format images. These images were collected using consumer grade trail cameras manufactured by Browning Trail Cameras. Cameras were installed across Mississippi (USA) in 2019 and 2020 from March through September. Images collected are exclusively oblique, unobstructed views of the sky. Cameras were placed in time-lapse mode and set to collect one image every hour. Our intent in this work was to first compare deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. Sky conditions, and specifically shadowing from clouds, are critical determinants …
Dataset For: Tracking An Oil Tanker Collision And Spilled Oils In The East China Sea Using Multisensor Day And Night Satellite Imagery, Shaojie Sun
C-IMAGE data
In this dataset, we used a multi-sensor day and night satellite approach to track the SANCHI oil tanker collision and oil spill event in January 2018 in the East China Sea. The drifted on fire oil tanker was tracked by Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product and Day/Night Band (DNB) imagery. Such pathway and locations were also reproduced with a numerical model, with RMS error of < 15 km. MultiSpectral Instrument (MSI) optical imagery during daytime shows smokes on 13 January 2018, further confirms the drifted tanker location. MSI imagery after 4 days of the tanker’s sinking (18 January 2018) reveals oil on the ocean surface to the east and northeast of the tanker sinking location. This combination of all available remote sensing and modeling techniques can provide effective means to monitor marine accidents and oil spills to assist event response.
Landsat Based Sargassum Coverage In The Northern Gulf Of Mexico, 2010, Chuanmin Hu, Lian Feng
Landsat Based Sargassum Coverage In The Northern Gulf Of Mexico, 2010, Chuanmin Hu, Lian Feng
C-IMAGE data
This dataset contains the sargassum coverage in the northeastern Gulf of Mexico detected using both Landsat remote sensing images and airborne AVIRIS data. The mean Sargassum coverage during the four quarters of 2010 for the study region are also provided. Dataset results are provided in Excel format and correspond to the the publication: Hu, C.; Hardy, R.; Ruder, E.; Geggel, A.; Feng, L.; Powers, S.; Hernandez, F.; Graettinger, G.; Bodnar, J.; McDonald, T. (2016). Sargassum coverage in the northeastern Gulf of Mexico during 2010 from Landsat and airborne observations: Implications for the Deepwater Horizon oil spill impact assessment. Marine Pollution …
Sargassum Detection Using Modis, Aviris, Landsat, And Hico Imagery, Chuanmin Hu, Lian Feng
Sargassum Detection Using Modis, Aviris, Landsat, And Hico Imagery, Chuanmin Hu, Lian Feng
C-IMAGE data
This dataset includes Moderate Resolution Imaging Spectroradiometer (MODIS), Airborne Visible-InfraRed Imaging Spectrometer (AVIRIS), Landsat and Hyperspectral Imager for the Coastal Ocean (HICO) images used in the paper: Hu, C., Feng, L., Hardy, R. F., & Hochberg, E. J. (2015). Spectral and spatial requirements of remote measurements of pelagic Sargassum macroalgae. Remote Sensing of Environment, 167, 229–246. doi:10.1016/j.rse.2015.05.022, together with the delineated sargassum features. The in situ spectral measurements used as endmembers to simulate the results are also included.
Moderate Resolution Imaging Spectroradiometer (Modis) Surface Oil Products For The Gulf Of Mexico, April - July 2010, Chuanmin Hu, Lian Feng
Moderate Resolution Imaging Spectroradiometer (Modis) Surface Oil Products For The Gulf Of Mexico, April - July 2010, Chuanmin Hu, Lian Feng
C-IMAGE data
This dataset contains the MODIS surface oil products (with thickness of >0 but 8um as thick) for all the golden days between April and July of 2010.
What Should Go In A Wildlife Professional’S Geospatial Toolbox? (Response Data), William Bean, Ryan C. Baumbusch, Brooke Berger, Matthew Delheimer, Lee J. Hecker, Matthew Lau, Megan C. Milligan
What Should Go In A Wildlife Professional’S Geospatial Toolbox? (Response Data), William Bean, Ryan C. Baumbusch, Brooke Berger, Matthew Delheimer, Lee J. Hecker, Matthew Lau, Megan C. Milligan
Research Data Sets
Geospatial tools have become a critical component to most wildlife studies and management questions. With a diversity of approaches available, current and future wildlife professionals deserve guidance on the most important tools to answer these questions. Younger professionals may be expected to know a separate set of skills from those required further on in their career. We conducted an online survey and a year-long search of job advertisements to identify the most important geospatial approaches, techniques, programs, and ancillary skills for wildlife professionals. We provide the results of these 2 efforts so that wildlife professionals interested in geospatial tools can …
Land Cover Data For The Mississippi-Alabama Barrier Islands, 2010-2011 Arcgis V10.3 Geodatabase, Gregory A. Carter, Carlton P. Anderson, Kelly L. Lucas, Nathan L. Hopper
Land Cover Data For The Mississippi-Alabama Barrier Islands, 2010-2011 Arcgis V10.3 Geodatabase, Gregory A. Carter, Carlton P. Anderson, Kelly L. Lucas, Nathan L. Hopper
Land Cover Data for the Mississippi-Alabama Barrier Islands, 2010-2011
Land cover on the Mississippi-Alabama barrier islands was surveyed in 2010-2011 as part of continuing research on island geomorphic and vegetation dynamics following the 2005 impact of Hurricane Katrina. Results of the survey include sub-meter GPS location, a listing of dominant vegetation species and field photographs recorded at 375 sampling locations distributed among Cat, West Ship, East Ship, Horn, Sand, Petit Bois and West Dauphin Islands. The survey was conducted in a period of intensive remote sensing data acquisition over the northern Gulf of Mexico by federal, state and commercial organizations in response to the 2010 Macondo Well (Deepwater Horizon) …
Satellite, Glider Data, And Field Measurements To Study Harmful Algae On The West Florida Shelf During 2011 And 2012, Chuanmin Hu
Satellite, Glider Data, And Field Measurements To Study Harmful Algae On The West Florida Shelf During 2011 And 2012, Chuanmin Hu
C-IMAGE data
Satellite Oceanography is a sub-task under Task 4. It relies primarily on satellite-collected data, processed using either NASA standard algorithms or customized algorithms to produce data and imagery products specifically tailored for this project.