henrique
/ imagenet1000_clsidx_to_labels.json
Last active
July 27, 2021 14:33
ImageNet 1k (ILSVRC2012) classes and WordNet synsets
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| { | |
| "0": "tench, Tinca tinca", | |
| "1": "goldfish, Carassius auratus", | |
| "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", | |
| "3": "tiger shark, Galeocerdo cuvieri", | |
| "4": "hammerhead, hammerhead shark", | |
| "5": "electric ray, crampfish, numbfish, torpedo", | |
| "6": "stingray", | |
| "7": "cock", | |
| "8": "hen", |
henrique
/ cifar100.json
Last active
July 25, 2021 15:38
CIFAR100 classes list (and 20 superclasses) as in https://www.cs.toronto.edu/~kriz/cifar.html
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| { | |
| "classes": ["apple", "aquarium_fish", "baby", "bear", "beaver", "bed", "bee", "beetle", "bicycle", "bottle", "bowl", "boy", "bridge", "bus", "butterfly", "camel", "can", "castle", "caterpillar", "cattle", "chair", "chimpanzee", "clock", "cloud", "cockroach", "couch", "crab", "crocodile", "cup", "dinosaur", "dolphin", "elephant", "flatfish", "forest", "fox", "girl", "hamster", "house", "kangaroo", "keyboard", "lamp", "lawn_mower", "leopard", "lion", "lizard", "lobster", "man", "maple_tree", "motorcycle", "mountain", "mouse", "mushroom", "oak_tree", "orange", "orchid", "otter", "palm_tree", "pear", "pickup_truck", "pine_tree", "plain", "plate", "poppy", "porcupine", "possum", "rabbit", "raccoon", "ray", "road", "rocket", "rose", "sea", "seal", "shark", "shrew", "skunk", "skyscraper", "snail", "snake", "spider", "squirrel", "streetcar", "sunflower", "sweet_pepper", "table", "tank", "telephone", "television", "tiger", "tractor", "train", "trout", "tulip", "turtle", "wardrobe", "whale", "willow_tree", "wolf", " |
henrique
/ gee_reduce_n_reproject.js
Created
January 4, 2020 12:17
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| // Make a suitable image for `reduceConnectedComponents()` | |
| // by adding a label band to the `img_pop3` image. | |
| img_pop3 = img_pop3.addBands(patchid.select('labels')); | |
| // Calculate the total population in demand area | |
| // defined by the previously added "labels" band | |
| // and reproject to original scale | |
| var patchPop = img_pop3.reduceConnectedComponents({ | |
| reducer: ee.Reducer.sum(), | |
| labelBand: 'labels', |
henrique
/ gee_segmentation_n_patches.js
Last active
January 4, 2020 12:22
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| // GMeans Segmentation | |
| var seg = ee.Algorithms.Image.Segmentation.GMeans(smooth.gt(demand_threshold), 3, 50, 10); | |
| Map.addLayer(seg.randomVisualizer(), {opacity:0.5}, 'GMeans Segmentation'); | |
| // SNIC Segmentation | |
| var snic = ee.Algorithms.Image.Segmentation.SNIC(smooth.gt(demand_threshold), 30, 0, 8, 300); | |
| Map.addLayer(snic.randomVisualizer(), {opacity:0.5}, 'SNIC Segmentation'); | |
| // Uniquely label the patches and visualize. | |
| var patchid = smooth.gt(demand_threshold) | |
| .connectedComponents(ee.Kernel.plus(1), 256); |
henrique
/ gee_electricity_demand_nga.js
Last active
January 4, 2020 11:56
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| // GRID3 population data | |
| var img_pop3 = ee.ImageCollection('users/henrique/GRID3_NGA_PopEst_v1_1_mean_float') | |
| // Nigerian nightlights (1Y median) | |
| var nighttime = ee.ImageCollection('NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG') | |
| .filter(ee.Filter.date('2018–09–01', '2019–09–30')) | |
| .median() | |
| .select('avg_rad') | |
| .clipToCollection(nigeria); | |
| // Demand layer | |
| var demand = img_pop3.gte(pop_threshold) // threshold population |
henrique
/ gist:f8c45f0e7aa09d72c3b8
Last active
August 29, 2015 14:22
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| [ | |
| { | |
| "number": 0, | |
| "timestamp": 1433080800 | |
| }, | |
| { | |
| "number": -6, | |
| "timestamp": 1433084400 | |
| }, | |
| { |