***my name is mohamed sobhi and iam study in faculty in navigation science and space technology****
**************Identifying Ship in Satellite Images **************
***********************Package: tensorfow ***********************
**********************Algorithm: CNN Model **********************
***************Dataset: ships from satellite data.***************
*********************Model selection: keras**********************

Dataset.
I'm using dataset from kaggle.
https://www.kaggle.com/rhammell/ships-in-satellite-imagery/code

Context
Satellite imagery provides unique insights into various markets, including agriculture, defense and intelligence, energy, and finance. New commercial imagery providers, such as Planet, are using constellations of small satellites to capture images of the entire Earth every day.

This flood of new imagery is outgrowing the ability for organizations to manually look at each image that gets captured, and there is a need for machine learning and computer vision algorithms to help automate the analysis process.

The aim of this dataset is to help address the difficult task of detecting the location of large ships in satellite images. Automating this process can be applied to many issues including monitoring port activity levels and supply chain analysis.

Content
The dataset consists of image chips extracted from Planet satellite imagery collected over the San Francisco Bay and San Pedro Bay areas of California. It includes 4000 80x80 RGB images labeled with either a "ship" or "no-ship" classification. Image chips were derived from PlanetScope full-frame visual scene products, which are orthorectified to a 3 meter pixel size.

Provided is a zipped directory shipsnet.zip that contains the entire dataset as .png image chips. Each individual image filename follows a specific format: {label} __ {scene id} __ {longitude} _ {latitude}.png

label: Valued 1 or 0, representing the "ship" class and "no-ship" class, respectively.
scene id: The unique identifier of the PlanetScope visual scene the image chip was extracted from. The scene id can be used with the Planet API to discover and download the entire scene.
longitude_latitude: The longitude and latitude coordinates of the image center point, with values separated by a single underscore.
The dataset is also distributed as a JSON formatted text file shipsnet.json. The loaded object contains data, label, scene_ids, and location lists.

The pixel value data for each 80x80 RGB image is stored as a list of 19200 integers within the data list. The first 6400 entries contain the red channel values, the next 6400 the green, and the final 6400 the blue. The image is stored in row-major order, so that the first 80 entries of the array are the red channel values of the first row of the image.

The list values at index i in labels, scene_ids, and locations each correspond to the i-th image in the data list.