A Satellite Imagery, Ecosystem-Based GIS Study of Bluefin Tuna and Right Whale Distribution and Movements in the Gulf of Marine and NW Atlantic

2002 ◽  
Author(s):  
Molly Lutcavage ◽  
Scott D. Kraus
Keyword(s):  
Satellite Imagery ◽  
Bluefin Tuna ◽  
Right Whale

Advancing an Ecosystem Approach in the Gulf of Maine

2012 ◽  
Keyword(s):  
Annual Cycle ◽  
Gulf Of Maine ◽  
Bluefin Tuna ◽  
Ecosystem Approach ◽  
Life Cycles ◽  
Eubalaena Glacialis ◽  
Long Distance ◽  
North Atlantic Right Whale ◽  
Right Whale

<i>Abstract</i> .—An ecosystem-based framework for managing the Gulf of Maine ecosystem needs to include the long-distance migrant species that use the system for only a part of their annual cycle. The numbers of cetaceans and seabirds in this category greatly outnumber those that breed within the gulf and risk being neglected in any framework that does not consider them explicitly. I review the role of the gulf in the life cycles of five species of cetacean, 18 seabirds or shorebirds, and five species of fish; the familiar iconic species, such as North Atlantic right whale <i>Eubalaena glacialis</i> , bluefin tuna <i>Thunnus thynnus</i> , phalaropes (red-necked phalarope <i>Phalaropus lobatus </i> and red phalarope <i>P. fulicaria</i> ), and sandpipers <i>Calidris </i> spp., are flagships for many more species for which the Gulf of Maine plays an irreplaceable part in the annual cycle.

Behavioural development in southern right whale calves

2019 ◽  
Vol 629 ◽  
pp. 219-234 ◽  
Author(s):  
MLK Nielsen ◽  
KR Sprogis ◽  
L Bejder ◽  
PT Madsen ◽  
F Christiansen
Keyword(s):  
Behavioural Development ◽  
Right Whale

LambdaNet: A Fully Convolutional Architecture for Directional Change Detection

2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Classification Scheme ◽  
Deep Architecture ◽  
Binary Process ◽  
Directional Change ◽  
Class Labels ◽  
Image Pairs

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

Mask R-CNN based segmentation method for satellite imagery of photovoltaics generation systems

2020 ◽  
Author(s):  
SiMing Liang ◽  
FengYang Qi ◽  
YiFan Ding ◽  
Rui Cao ◽  
Qiang Yang ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Segmentation Method
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