آیا چشمان شما سوزش دارد؟

سوزش چشم، سرخی، خارش، خشکی و احساس خستگی ممکن است نشانه مشکلات مختلف چشمی باشد. بی‌توجهی به این علایم می‌تواند سلامت بینایی شما را تحت تأثیر قرار دهد.

در مرکز تخصصی چشم رضوان، با معاینات دقیق و خدمات تخصصی، برای تشخیص علت ناراحتی‌های چشمی و انتخاب روش مناسب تداوی در کنار شما هستیم.

سلامت چشمان‌تان را جدی بگیرید؛ برای معاینه تخصصی مراجعه کنید.

شعبه کابل: کابل، دشت برچی، پل خشک، جوار شفاخانه عالمی، مارکیت توحید، منزل دوم تماس و واتساپ: 0773595032 | 0775713021

شعبه دایکندی: شهرک جدید نیلی، مارکیت شهر، زیر عزیزی‌بانک تماس: 0776680870 | 0778005008

وب‌سایت: www.rezwaneye.com

مرکز تخصصی چشم رضوان؛ بینایی برای یک عمر

مرکز_تخصصی_چشم_رضوان #سوزش_چشم #سلامت_چشم #معاینه_چشم #چشم_پزشکی

Hier soir, j’ai présenté dans l’amphithéâtre C002 au sein du campus de enseeiht à Toulouse, « Contribuez à OSM ». La bonne surprise fut le taux de remplissage de la salle. J’ai compté plus d’une vingtaine de personnes. La présentation était articulée en deux parties : la première, un tout petit peu de théorie et pas de présentation d’outils ; la deuxième, des démonstrations, des petits trucs faciles pour commencer, puis des trucs plus « grands » (de l’infrastructure). En trente minutes, j’avais plié ma présentation. Il restait vingt-cinq minutes, qui se sont très, très facilement remplies par de très nombreuses questions. Merci au plublic pour ces questions. Merci à l’organisation du Capitole du Libre. La présentation est disponible à https://files.hirlimann.net/talks/cdl2026/.

It’s been two months since I started to edit the Ilagan River again. While I had done so in the past, way before these two months, this period turned out to be a great adventure that I’m still hiking.

It was a time after Project CaCau. When I put it to an end, I felt not only disappointed, but ambitious. Why exactly? Well, I don’t know, but the roaring engine I had used to keep me going through that project was still on, and I couldn’t just stop it. So, I had to redirect it somewhere.

For a bit, I had just redirected it to OSM. I experimented with multiple things and tried to pick up my old stuff. But one in particular caught my attention. A while ago, I was editing the river area of the Ilagan River. I never picked it up again until then. Despite that, it just stuck. Starting in the 15th of September, I got into the flow of the edits, and I never stopped.

I didn’t just do the main river either. I had also started doing its tributaries because my satisfaction didn’t get put with just that. Then, suddenly, the Bintacan River got finished, then the Nanaguan, and then I was suddenly speeding through the headwaters of the Abuan. I was now pushing 2K-3K edits a day, with the little free time I had.

My satisfaction didn’t stop; I had also started mapping the landslide areas. Now, at that point, I was satisfied. I had forgotten Project CaCau and its shadow that burdened me. Then, I realized: why not make this run official? Make it a project? So, I did.

Enter, Project KarayanIlagan. It meant “Ilagan River” in Ilocano: Karayan as River, and Ilagan as itself. Nothing changed in my workflow; just that I finally had a name to keep it going and back it up, putting hashtag comments for it as #KarayanIlagan (and later on with #ChasingWaterways for my fellow mappers’ initiative), and to give it a history and recognition.

… See full entry

I have custom branch of EveryDoor since July. Namely because for recycling containers i need tagging preset with custom field types not supported by iD schema and for ED plugin engine which has a annoying bug regarding NeedsInfo selectors.
Opening and searching presets was occasionally super slow and past few nights ive tried to fix annoyances. Turned out the performance fix was trivial as i had compiled app with wrong parameter which caused app to not be able to compile something related to presets lookup.

Anyways, 2 nights later, achievements include:

• saving downloaded map data at least 20x faster (by using already existing db index which existing query disregarded)
• zoom no longer jams at zoom level 15. Technical reason is that ED has internally 2 map views for different zoom ranges and ones min and other’s max zoom was lvl 15.
• amenities list scrolling is now vertical instead horizontal (which i thought was unintuitive and waste of space)
• tiles are loaded while zooming, in current version, after zooming tiles are downloaded only on map drag event. Fixed by updating map library.
• i discovered ED already has special overview map of downloaded osm data (dark-green rectangles at Menu -> Data Management -> Download tiles), but they are rather useless as they denote simply areas that was downloaded at some point. Merged rectangles into rasterized layer to show up to date and outdated areas as one.
• haven’t been able to test, but cached map tiles should cleared after 1 month to prevent out of date background layer. I have previously created multiple false positive notes about missing road or building only to find my map layer was outdated
• dragging to add new element while moving and tracking gps, won’t cause unexpected jumps and zoom changes as user’s location changes.
• few more changes related to map interactions i forgot

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Mi experiencia en el Mapatón Nacional de México 2025: cartografiar edificios para fortalecer la información territorial

¿Cómo podemos analizar los riesgos de una comunidad si ni siquiera contamos con información suficiente sobre las edificaciones que existen en su territorio?

Esta pregunta resume una de las razones por las que considero tan importante la cartografía abierta para la Gestión Integral del Riesgo de Desastres.

Durante agosto y septiembre de 2025 tuve la oportunidad de participar como voluntaria en el Mapatón Nacional de México 2025, una iniciativa de cartografía colaborativa vinculada con el Centro Nacional de Prevención de Desastres (CENAPRED), la Secretaría de Desarrollo Agrario, Territorial y Urbano (SEDATU), la Universidad Autónoma del Estado de México (UAEMéx) y Humanitarian OpenStreetMap Team (HOT).

El objetivo fue contribuir a la actualización y precisión de la cartografía base utilizada para la gestión del riesgo y la planeación territorial.

¿En qué consistió mi participación?

Mi actividad se concentró en la digitalización de edificaciones en localidades del estado de Chiapas, donde existían vacíos de información cartográfica.

A través de OpenStreetMap participamos en la identificación y representación de edificios, contribuyendo a mejorar la disponibilidad de información geoespacial abierta.

Aunque pudiera parecer una tarea sencilla, representar correctamente las edificaciones tiene implicaciones importantes para el conocimiento del territorio.

¿Qué relación tiene esto con la Gestión Integral del Riesgo?

Para analizar un riesgo necesitamos conocer, entre otros aspectos, las amenazas presentes, los elementos expuestos y sus condiciones de vulnerabilidad.

La cartografía de edificaciones constituye uno de los insumos que pueden utilizarse para identificar espacialmente elementos potencialmente expuestos a fenómenos como inundaciones, sismos o movimientos de ladera.

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Если вы картируете сельскую местность, вам знакома проблема: снимки Bing или Esri старые, в облаках или просто не показывают, асфальтирована дорога или грунтовая. Sentinel-2 во многом решает эту проблему. Это бесплатные спутниковые снимки, которые обновляются каждые несколько дней, с разрешением 10 м и инфракрасными каналами, невидимыми глазу.

В композиции ложных цветов (каналы B8, B4 и B3) растительность становится красной, а асфальт выглядит тёмным и контрастным. Асфальтированные дороги, пересекающие кустарник или поля, видны очень отчётливо.

Sentinel-2 в ложных цветах в JOSM, сельская местность штата Сеара, Бразилия. Содержит изменённые данные Copernicus Sentinel (2026). Изображение: Wikimedia Commons, CC BY-SA 4.0.

В этом руководстве я покажу, как добавить этот слой в JOSM с помощью бесплатного сервиса Copernicus Data Space Ecosystem Европейского союза.

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Si mapeas zonas rurales, conoces el problema: las imágenes de Bing o Esri son antiguas, tienen nubes o simplemente no muestran si un camino está asfaltado o es de tierra. Sentinel-2 resuelve buena parte de esto. Son imágenes satelitales gratuitas, actualizadas cada pocos días, con 10 m de resolución y bandas infrarrojas que el ojo no ve.

En la composición de falso color (bandas B8, B4 y B3), la vegetación se ve roja y el asfalto aparece oscuro y muy contrastado. Las carreteras pavimentadas que atraviesan el monte o los cultivos se destacan muchísimo.

Sentinel-2 en falso color en JOSM, zona rural de Ceará, Brasil. Contiene datos Copernicus Sentinel modificados (2026). Imagen: Wikimedia Commons, CC BY-SA 4.0.

En este tutorial muestro cómo añadir esta capa a JOSM usando el servicio gratuito Copernicus Data Space Ecosystem de la Unión Europea.

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Si vous cartographiez des zones rurales, vous connaissez le problème : l’imagerie Bing ou Esri est ancienne, nuageuse, ou ne permet tout simplement pas de savoir si une route est goudronnée ou en terre. Sentinel-2 règle une bonne partie de ce problème. C’est une imagerie satellite gratuite, mise à jour tous les quelques jours, avec une résolution de 10 m et des bandes infrarouges invisibles à l’œil nu.

Dans la composition fausses couleurs (bandes B8, B4 et B3), la végétation devient rouge et l’asphalte apparaît sombre et bien contrasté. Les routes goudronnées qui traversent la brousse ou les cultures ressortent très nettement.

Sentinel-2 en fausses couleurs dans JOSM, zone rurale du Ceará, Brésil. Contient des données Copernicus Sentinel modifiées (2026). Image : Wikimedia Commons, CC BY-SA 4.0.

Ce tutoriel montre comment ajouter cette couche à JOSM avec le service gratuit Copernicus Data Space Ecosystem de l’Union européenne.

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If you map rural areas, you know the problem: Bing or Esri imagery is old, cloudy, or simply doesn’t show whether a road is paved or dirt. Sentinel-2 solves a good part of that. It is free satellite imagery, refreshed every few days, with 10 m resolution and infrared bands the human eye can’t see.

In the false color composite (bands B8, B4 and B3), vegetation turns red and asphalt turns dark and high-contrast. Paved roads crossing scrubland or farmland really stand out.

Sentinel-2 false color in JOSM, rural Ceará, Brazil. Contains modified Copernicus Sentinel data (2026). Image: Wikimedia Commons, CC BY-SA 4.0.

This tutorial shows how to add this layer to JOSM using the free Copernicus Data Space Ecosystem service from the European Union.

Important limitation: 10 m per pixel works for highways, wide rural roads, water, vegetation, land use and burned areas. It does not work for buildings or narrow urban streets.

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1. Create a Copernicus Data Space account

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Quem mapeia no interior do Brasil conhece o problema: a imagem do Bing ou da Esri é antiga, cheia de nuvem ou simplesmente não mostra se aquela estrada é asfaltada ou de terra. O Sentinel-2 resolve boa parte disso. É uma imagem de satélite gratuita, atualizada a cada poucos dias, com 10 m de resolução e com bandas de infravermelho que o olho não vê.

Na composição falsa cor (bandas B8, B4 e B3), a vegetação fica vermelha e o asfalto fica escuro e bem contrastado. Rodovias pavimentadas cortando a caatinga ou a lavoura ficam muito evidentes.

Sentinel-2 em falsa cor no JOSM, no interior do Ceará. Contém dados Copernicus Sentinel modificados (2026). Imagem: Wikimedia Commons, CC BY-SA 4.0.

Neste tutorial mostro como colocar essa camada no JOSM usando o serviço gratuito do Copernicus Data Space Ecosystem, da União Europeia.

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En se fiant aux courbes de niveaux entourant le sommet virtuel du Rocher de la Tête Noire (1688, Valais, Suisse) sur la carte OpenStreetMap on peut constater que le point (noeud ?) du sommet est mal placé, et qu’il devrait se trouver à environ 170 m plus loin au nord-est, à l’intérieur du cercle formé par la courbe 1680, seule zone 1680-1690 s’apparentant à un mont ou un sommet ; c’est d’ailleurs ce que confirme Swisstopo, la carte la plus fiable en Suisse.

本文使用 Geofabrik 的数据快照，从行政、道路交通、公共和商业设施、建筑和土地利用、标签等方面分析了 2013–2025 年中国大陆各乡镇 OSM 数据完备程度的增长情况，并以 2026 年 10 月 30 日的快照给出了 2026 年统计结果的前瞻。

以完备度这一指标作为参考，14 年来中国大陆的评分从 10.5 增长到 44.8，增长速度有先快后慢的趋势，原因主要是行政要素尤其是行政节点在近年趋于完善。道路交通一直是近年贡献增长最多的领域。城市的街道类型与乡村的镇、乡类型的得分差距逐年扩大，14 年间从 20 余分扩大到近 40 分。东部省份和部分拥有活跃绘图者的中西部省份是主要的增长地区，辽宁、湖北、海南和陕西四省的增长水平显著滞后于其初始水平。

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相关链接：

• 2025 年中国大陆乡镇 OSM 要素完备度分析报告（一）
• 2025 年中国大陆乡镇 OSM 要素完备度分析报告（二）
• 脚本：count_osm_feature.py（GPL-3.0）
• 数据：China_Report_2013–2025（GPL-3.0）
• 网站：OSMChina-coverage

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1 评价方法

1.1 数据来源

1.1.1 OSM 数据

和去年用发送数万次 Overpass 请求乡镇要素的笨办法不同，本文的作者不久前才注意到通过 Geofabrik 的数据快照来完成这一分析是更加方便的做法，后者也是 Taginfo 的数据来源。对于中国大陆，Geofabrik 自 2014 年起提供有每年 1 月 1 日的数据快照，大小仅 100 MB 到 1.5 GB 不等。除了节省下载周期、缓存体积，以及避免统计时刻前后不一的口径问题以外，Geofabrik 还存有完整的要素位置、标签和几何关系，支持统一的快速提取和复杂的判断操作。

1.1.2 乡镇名单

本文从 adyliu/china_area 与 xiangyuecn/AreaCity-JsSpider-StatsGov 的存档提取了 2013–2023 年的国家统计局统计用区划代码，并将其清洗为乡镇名单，清洗过程与先前日志所描述的相同。由于国家统计局在 2024 年后不再公开统计用区划代码，2024 年的名单沿用自 2023 年。同时，依社区朋友的建议，2025 年与 2026* 年的名单改用民政部国家地名信息库的乡镇名单。

乡镇名单的坐标大多取自其匹配到的 OSM 行政节点。早期的行政节点缺失较多，本文对其进行了跨年份的名称和坐标匹配。对于至今或至被撤销为止都没有对应 OSM 行政节点的乡镇，本文使用高德 API 补上了统计所需的先验坐标。

1.2 统计指标

借助 Geofabrik 中留有的完整的空间信息和标签信息，本文对旧标准中部分过于简单的统计规则进行了些许调整，在保证总分数相对不变的条件下细分了评分项目和计分方法。其中最主要的有三处：

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“The idea is pretty cool. It downloads regional data from Geofabrik directly in the browser and formats the data into Parquet format, then lets you ask the AI ​​to query it using DuckDB.”

“This project looks cool, even though I’m still struggling to understand what use case it is trying to solve. (What does “routable road network” even mean?) (But well.. Perhaps this is partly due to my own bias, as I’ve never worked on any OSM-based routing or simulation projects)”

“I see data export to SUMO and MATSim, and LinkedIn mentions the possibility of using NetworkX. Judging by the profile, the author is a researcher with a focus on graph algorithms.”

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“Export to PMTiles, MBTiles, GeoPackage, GeoJSON, kepler.gl, MATSim or SUMO”

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Machishirube (X-Gate Inc.) — organised editing activity

Organisation: X-Gate Inc. (X-Gate株式会社), Shinjuku, Tokyo, Japan Account: まちのお店しるべ (operated by X-Gate Inc.) — contact by OSM message Hashtag: #machishirube Period: from 2026-12 (ongoing) — no edits before two weeks after the announcement Area: Japan

Purpose

X-Gate Inc. helps local businesses in Japan keep their public information accurate. At the request and with the consent of each client business, we add the business to OpenStreetMap if it is missing, and correct its basic information if it is outdated.

What we edit

• Only businesses that are our clients and have consented.
• Only factual tags: name (as on the signboard), shop/amenity/craft/office tag, address (addr:*), phone, opening_hours, website. No promotional text.
• Closed or moved businesses are updated or removed promptly.

Sources

• The business’s official website and statements by the owner/manager. Positions come from the GSI (国土地理院) address search.
• We do not copy from Google Maps, Tabelog or any other proprietary source.
• The changeset tag source states the actual source (e.g. business_owner;website;GSI address search).

Method

• One business per changeset, through the OSM API v0.6 (OAuth 2).
• A staff member reviews every edit before upload; there is no unattended bulk upload, so this is neither an import nor an automated edit.
• Before adding, we check for existing objects nearby to avoid duplicates.
• Changeset comment: Add business info at owner's request #machishirube with a link to this page; hashtags=#machishirube.

Communication

• Announced on the talk-ja mailing list (Japanese) at least two weeks before the first edit.
• We answer questions and comments within 2 business days (Japanese or English).

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日本語

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The Dakota/Lakota language has been a written language for only the most recent chapters of its history. Writing systems for the language were first devised by 19th-century missionaries who sought to promote literacy as a part of encouraging Dakota people to convert to Christianity. These missionaries, especially Stephen Riggs, developed an alphabet based on the Latin Alphabet to represent the sounds present in the language. All widely used methods for writing the language today are based on Riggs’ system with some degree of variation.

There is no single writing system accepted by the vast majority of speaker communities. A variety of systems - or orthographies - exist with varying degrees of popularity, and invididual speakers and teachers often have strong preferences one way or another. Areas of debate include:

• May an orthography make use of special characters (ŋ, ǧ, ḳ, ú) or should writing be done only with characters that can be written with a standard English keyboard?
• The language contains vowels (a, i, u) that may be nasalized or not, depending on context; should the orthography note the difference?
• The language contains consonants (z, c, p, t, k, s) that may be aspirated for a harder sound or unaspirated; should the orthography note the difference?
• The language contains consonants (g, h) that may be guttural or not; should the orthography note when this happens?
• Some words are distinguished from other words only by which syllable is emphasized when the word is spoken e.g. “máġa” is a field while “maġá” is a goose. Should the orthography note which syllable is emphasized or not?

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Dakota (instead called Lakota by speakers of that dialect) is the indigenous language of the Oc̣eṭi Ṡakowiƞ (Seven Council Fires) people of North America, consisting of the Bdewaḳaƞṭuƞwaƞ, Waḣpeḳute, Waḣpeṭuƞwaƞ, Sisiṭuƞwaƞ, Ihaƞkṭuƞwaƞ, Ihaƞkṭuƞwaƞna and Ṭiṭuƞwaƞ communities. Most members of these communities will describe themselves as Dakota (or Lakota for the Ṭiṭuƞwaƞ) people.

Dakota and Lakota communities can be found in Minnesota, North Dakota, South Dakota, Nebraska, Montana, Manitoba and Saskatchewan. All of these places have a rich geography of names devised by Oc̣eṭi Ṡakowiƞ people in the Dakota/Lakota language, including rivers, lakes, towns, islands, hills and more. People have traveled since the dawn of human history and some areas once had Dakota/Lakota communities that presently do not, and so other areas (such as Wisconsin, Iowa, Wyoming and Ontario) also have locations with names in the language.

Historically, there have been far, far more Dakota and Lakota place names than those that survive for us to learn about them in the present. Colonization has inflicted enormous trauma and language loss on these and other Native communities through allegedly-voluntary but actually forced exile from their lands, wars of conquest, traumatic boarding school assimilation, and more. However, many people who are members of (or in close contact with) these communities have put in heroic efforts in defense of the language. These language protectors have striven to continue learning, speaking and teaching the language, and to record many aspects of the language for future generations - including place names.

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My mapping on OpenStreetMap includes names in both Dakota and Lakota, with ISO codes dak and lkt respectively. This is an unusual approach compared to a lot of other multilingual mapping, so it’s worth explaining in more detail.

Dakota and Lakota are two different dialects of the same language (and are two possible names for that language). They are mutually intelligible, and a fluent speaker of one can understand a fluent speaker of the other without difficulty. An analogy might be made to the Spanish spoken in Mexico vs the Spanish spoken in Spain - a speaker of the other dialect will seem to have a strong accent, unusual pronunciation, and some different word choices, but they are still clearly speaking your language. Both dialects are endangered, but there are substantially more Lakota speakers than Dakota speakers and this has been true for a long time.

Dakota and Lakota share the vast majority of their vocabulary. Most words that differ between these dialects differ in predictable, formulaic ways that do not represent any difference in meaning (e.g. Oṭuƞwe vs Otȟúŋwahe or Bde vs Ble). The most meaningful differences in Lakota vs Dakota speech emerge in conversational flow, which rarely matters for place names. Additionally, names of places in Dakota/Lakota are rarely Proper Nouns as English speakers understand them (words that are only used as names e.g. London, Florida, Catherine, Jesse). Rather, place names in the language - like the names of people in the language - are constructed from normal words.

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There are about 450,000 open notes on OpenStreetMap. Each one is a message from someone who saw a problem on the map. But to know what a note needs, you have to open it and read it, one by one. That takes a lot of time.

So I trained a machine learning model to do this first read for us. I also made a simple website to show the results:

→ https://osm-notes.rub21.com

What does the model do?

The model reads the first comment of each note and gives it two labels:

• Category: what the mapper should do. For example: missing place, place closed or changed, wrong name, missing or wrong road, other wrong data, spam, empty, or not mappable (opinions, questions).
• Theme: what the note is about. For example: food, shops, health, roads, transport.

How I built it

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A few weeks ago I wrote here about Wandering Around, a free browser game in which you can walk through any town or city on Earth, built on the fly from OpenStreetMap. Thanks to your comments and feedback, the game has now left beta: version 1.0 is live.

Play it here: https://wanderingaround.games (phone and desktop, no download, no account needed)

This post is about what changed on the OSM side since then.

Buildings that look like the real thing

• Churches are built from their real outline. The game reads the shape of the building and finds the nave, the transepts and the choir, and each part gets its own roof that follows the shape. A square part that sticks out becomes the tower. A church mapped carefully as a cross shape now also looks like one at eye level.
• Mosques get a dome on a drum, pointed windows and a minaret. They are recognised by religion=muslim, building=mosque or the name.
• Cemeteries (landuse=cemetery) are laid out with gravel paths, rows of graves in neutral shapes, low hedges and trees.
• Sidewalks now run along the whole street as one continuous strip. Short gaps are filled and corners join neatly.
• Shop fronts, front doors and porches get their own place in the facade, so windows don’t end up behind a door.

Walking along OSM streets

When you tap somewhere on the map, your avatar now finds the way over the OSM road and path network: tiles stitched together, loose ends of footpaths connected, no private roads and no tunnels. Before, it walked in a straight line and got stuck against the first wall. If the straight line is free it still walks straight, so it doesn’t make strange detours across an open square.

Trains and ferries, everywhere

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According to sources (such as this a general rule of thumb for roads within urban areas is 40 for busy traffic roads and 60 for main roads. I believe that the highway=’primary’ tag should generally align for these roads (usually long stretching across a district, 2-3 lanes) such as Ramsis St. For the less important roads they should be secondary of tertiary depending on importance, where tertiary is more likely to intersect more with busier residential areas and roads. This can help routing algorithms better impute speeds depending on the highway, helping in this region as road speeds are rarely defined for non-trunk roads.