Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mapping croplands is a challenging problem in a context of climate change and evolving agricultural calendars. Classification based on MODIS vegetation index time series is performed in order to map crop types in the Brazilian state of Mato Grosso.  used the recently developed Dense Bag-of-Temporal-SIFT-Words algorithm, which is able to capture temporal locality of the data. It allows the accurate detection of around 70% of the agricultural areas. It leads to better classification rates than a baseline algorithm, discriminating more accurately classes with similar profiles.
['Adeline Bailly', 'Damien Arvor', 'Laetitia Chapel', 'Romain Tavenard']
I am writing a research paper on a idea Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon
[{"from": "human", "value": "I am writing a research paper on a idea Classification of MODIS time series with Dense Bag-of-Temporal-SIFT-Words: Application to cropland mapping in the Brazilian Amazon"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Mapping croplands is a challenging problem in a context of climate change and evolving agricultural calendars. Classification based on MODIS vegetation index time series is performed in order to map crop types in the Brazilian state of Mato Grosso.  used the recently developed Dense Bag-of-Temporal-SIFT-Words algorithm, which is able to capture temporal locality of the data. It allows the accurate detection of around 70% of the agricultural areas. It leads to better classification rates than a baseline algorithm, discriminating more accurately classes with similar profiles."}]